diff --git a/.gitignore b/.gitignore
index 3fcc055cddae5a221d590a8c3eae393b73251e77..043cbe7fbfebbadf7adb95ee0dfb136cf50fbac9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -81,12 +81,12 @@ tests/test-tokenizer-0
 koboldcpp.so
 koboldcpp_failsafe.so
 koboldcpp_openblas.so
-koboldcpp_openblas_noavx2.so
+koboldcpp_noavx2.so
 koboldcpp_clblast.so
 koboldcpp.dll
 koboldcpp_failsafe.dll
 koboldcpp_openblas.dll
-koboldcpp_openblas_noavx2.dll
+koboldcpp_noavx2.dll
 koboldcpp_clblast.dll
 koboldcpp_cublas.dll
 cublas64_11.dll
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9a2c653d746ade027b33f4fd228c562bea3d9abe..103abe3101744bf32b53c5701adbefca28adfb9f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -3,9 +3,9 @@
 # IT WILL NOT BE UPDATED OR MAINTAINED !!!
 
 message(STATUS "============== ============== ==============")
-message(STATUS "WARNING! Do NOT use this file. It is UNSUPPORTED for normal users. Use MAKE instead.")
-message(STATUS "It is ONLY for CUBLAS build testing on windows visual studio. IT WILL NOT BE UPDATED OR MAINTAINED !!!")
-message(STATUS "IF YOU ARE SEEING THIS, you MUST ONLY be building AN EXPERIMENAL WINDOWS CUBLAS BUILD! NOTHING ELSE WILL BE SUPPORTED !!!")
+message(STATUS "WARNING! Recommend NOT to use this file. It is UNSUPPORTED for normal users. Use MAKE instead.")
+message(STATUS "It is ONLY for CUBLAS builds on windows visual studio. IT WILL OVERWRITE YOUR EXISTING MAKEFILE !!!")
+message(STATUS "IF YOU ARE SEEING THIS, you MUST ONLY be building CUBLAS BUILDS! NOTHING ELSE WILL BE SUPPORTED !!!")
 message(STATUS "============== ============== ==============")
 
 cmake_minimum_required(VERSION 3.12) # Don't bump this version for no reason
@@ -43,11 +43,12 @@ if (NOT MSVC)
 endif()
 
 # 3rd party libs
-option(LLAMA_CUBLAS                          "llama: use cuBLAS"                                ON)
+option(LLAMA_CUBLAS                          "llama: use CUDA"                                ON)
+set(LLAMA_CUDA_MMQ_Y       "64" CACHE STRING "llama: y tile size for mmq CUDA kernels")
 set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
 set(LLAMA_CUDA_DMMV_Y       "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
 set(LLAMA_CUDA_MMV_Y        "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
-option(LLAMA_CUDA_DMMV_F16                   "llama: use 16 bit floats for dmmv CUDA kernels"   OFF)
+option(LLAMA_CUDA_F16                        "llama: use 16 bit floats for dmmv CUDA kernels"   OFF)
 set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
 option(LLAMA_K_QUANTS                        "llama: use k-quants"                              ON)
 
@@ -79,13 +80,15 @@ if (LLAMA_CUBLAS)
         set(GGML_V2_LEGACY_CUDA_SOURCES otherarch/ggml_v2-cuda-legacy.cu otherarch/ggml_v2-cuda-legacy.h)
 
         add_compile_definitions(GGML_USE_CUBLAS)
+        #add_compile_definitions(GGML_CUDA_CUBLAS) #remove to not use cublas
+        add_compile_definitions(GGML_CUDA_MMQ_Y=${LLAMA_CUDA_MMQ_Y})
         #add_compile_definitions(GGML_CUDA_FORCE_DMMV) #non dmmv broken for me
 
         add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
         add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
         add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
-        if (LLAMA_CUDA_DMMV_F16)
-            add_compile_definitions(GGML_CUDA_DMMV_F16)
+        if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
+            add_compile_definitions(GGML_CUDA_F16)
         endif()
         add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
 
@@ -96,10 +99,19 @@ if (LLAMA_CUBLAS)
         endif()
 
     if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
-        if (LLAMA_CUDA_DMMV_F16)
-            set(CMAKE_CUDA_ARCHITECTURES "60;61") # needed for f16 CUDA intrinsics
+        # 52 == lowest CUDA 12 standard
+        # 60 == f16 CUDA intrinsics
+        # 61 == integer CUDA intrinsics
+        # 70 == (assumed) compute capability at which unrolling a loop in mul_mat_q kernels is faster
+        if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
+            set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics
         else()
-            set(CMAKE_CUDA_ARCHITECTURES "37;52;61") # lowest CUDA 12 standard + lowest for integer intrinsics
+            message("CUDA Toolkit Version: ${CUDAToolkit_VERSION}")
+            if(CUDAToolkit_VERSION VERSION_GREATER 12)
+                 set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
+            else()
+                 set(CMAKE_CUDA_ARCHITECTURES "37;52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
+            endif()
         endif()
     endif()
     message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
@@ -120,6 +132,7 @@ if (LLAMA_ALL_WARNINGS)
             -Wshadow
             -Wstrict-prototypes
             -Wpointer-arith
+            -Wmissing-prototypes
         )
         set(cxx_flags
             -Wall
@@ -259,6 +272,8 @@ endif()
 add_library(ggml OBJECT
             ggml.c
             ggml.h
+            ggml-alloc.c
+            ggml-alloc.h
             k_quants.h
             k_quants.c
             ${GGML_SOURCES_CUDA})
diff --git a/Makefile b/Makefile
index f2386457f942a9f778949c626090113276962fbf..8b0693dd303802f06ae45d5a9b8dece8d37842fe 100644
--- a/Makefile
+++ b/Makefile
@@ -1,4 +1,4 @@
-default: koboldcpp koboldcpp_failsafe koboldcpp_openblas koboldcpp_openblas_noavx2 koboldcpp_clblast koboldcpp_cublas
+default: koboldcpp koboldcpp_failsafe koboldcpp_openblas koboldcpp_noavx2 koboldcpp_clblast koboldcpp_cublas
 tools: quantize_gpt2 quantize_gptj quantize_llama quantize_neox quantize_mpt
 dev: koboldcpp_openblas
 dev2: koboldcpp_clblast
@@ -42,7 +42,7 @@ endif
 
 # keep standard at C11 and C++11
 CFLAGS   = -I.              -I./include -I./include/CL -I./otherarch -I./otherarch/tools -Ofast -DNDEBUG -std=c11   -fPIC -DGGML_USE_K_QUANTS
-CXXFLAGS = -I. -I./examples -I./include -I./include/CL -I./otherarch -I./otherarch/tools -O3 -DNDEBUG -std=c++11 -fPIC -DGGML_USE_K_QUANTS
+CXXFLAGS = -I. -I./examples -I./include -I./include/CL -I./otherarch -I./otherarch/tools -Ofast -DNDEBUG -std=c++11 -fPIC -DGGML_USE_K_QUANTS
 LDFLAGS  =
 
 # these are used on windows, to build some libraries with extra old device compatibility
@@ -165,20 +165,34 @@ else ifdef LLAMA_CUDA_DMMV_Y
 else
 	NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
 endif # LLAMA_CUDA_MMV_Y
+ifdef LLAMA_CUDA_F16
+	NVCCFLAGS += -DGGML_CUDA_F16
+endif # LLAMA_CUDA_F16
 ifdef LLAMA_CUDA_DMMV_F16
-	NVCCFLAGS += -DGGML_CUDA_DMMV_F16
+	NVCCFLAGS += -DGGML_CUDA_F16
 endif # LLAMA_CUDA_DMMV_F16
 ifdef LLAMA_CUDA_KQUANTS_ITER
 	NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
 else
 	NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
 endif
+ifdef LLAMA_CUDA_MMQ_Y
+	NVCCFLAGS += -DGGML_CUDA_MMQ_Y=$(LLAMA_CUDA_MMQ_Y)
+else
+	NVCCFLAGS += -DGGML_CUDA_MMQ_Y=64
+endif # LLAMA_CUDA_MMQ_Y
+#ifdef LLAMA_CUDA_CUBLAS
+#	NVCCFLAGS += -DGGML_CUDA_CUBLAS
+#endif # LLAMA_CUDA_CUBLAS
+ifdef LLAMA_CUDA_CCBIN
+	NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
+endif
 ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
-	$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) $(CUBLAS_FLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
+	$(NVCC) $(NVCCFLAGS) $(subst -Ofast,-O3,$(CXXFLAGS)) $(CUBLAS_FLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
 ggml_v2-cuda.o: otherarch/ggml_v2-cuda.cu otherarch/ggml_v2-cuda.h
-	$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) $(CUBLAS_FLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
+	$(NVCC) $(NVCCFLAGS) $(subst -Ofast,-O3,$(CXXFLAGS)) $(CUBLAS_FLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
 ggml_v2-cuda-legacy.o: otherarch/ggml_v2-cuda-legacy.cu otherarch/ggml_v2-cuda-legacy.h
-	$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) $(CUBLAS_FLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
+	$(NVCC) $(NVCCFLAGS) $(subst -Ofast,-O3,$(CXXFLAGS)) $(CUBLAS_FLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
 endif # LLAMA_CUBLAS
 
 ifdef LLAMA_METAL
@@ -213,7 +227,7 @@ endif
 DEFAULT_BUILD =
 FAILSAFE_BUILD =
 OPENBLAS_BUILD =
-OPENBLAS_NOAVX2_BUILD =
+NOAVX2_BUILD =
 CLBLAST_BUILD =
 CUBLAS_BUILD =
 
@@ -221,7 +235,7 @@ ifeq ($(OS),Windows_NT)
 	DEFAULT_BUILD = $(CXX) $(CXXFLAGS)  $^ -shared -o $@.dll $(LDFLAGS)
 	FAILSAFE_BUILD = $(CXX) $(CXXFLAGS) $^ -shared -o $@.dll $(LDFLAGS)
 	OPENBLAS_BUILD = $(CXX) $(CXXFLAGS) $^ lib/libopenblas.lib -shared -o $@.dll $(LDFLAGS)
-	OPENBLAS_NOAVX2_BUILD = $(CXX) $(CXXFLAGS) $^ lib/libopenblas.lib -shared -o $@.dll $(LDFLAGS)
+	NOAVX2_BUILD = $(CXX) $(CXXFLAGS) $^ -shared -o $@.dll $(LDFLAGS)
 	CLBLAST_BUILD = $(CXX) $(CXXFLAGS) $^ lib/OpenCL.lib lib/clblast.lib -shared -o $@.dll $(LDFLAGS)
 
 ifdef LLAMA_CUBLAS
@@ -233,7 +247,7 @@ else
 	FAILSAFE_BUILD = $(CXX) $(CXXFLAGS) $^ -shared -o $@.so $(LDFLAGS)
 	ifdef LLAMA_OPENBLAS
 	OPENBLAS_BUILD = $(CXX) $(CXXFLAGS) $^ $(ARCH_ADD) -lopenblas -shared -o $@.so $(LDFLAGS)
-	OPENBLAS_NOAVX2_BUILD = $(CXX) $(CXXFLAGS) $^ $(ARCH_ADD) -lopenblas -shared -o $@.so $(LDFLAGS)
+	NOAVX2_BUILD = $(CXX) $(CXXFLAGS) $^ $(ARCH_ADD) -lopenblas -shared -o $@.so $(LDFLAGS)
 	endif
 	ifdef LLAMA_CLBLAST
         ifeq ($(UNAME_S),Darwin)
@@ -283,8 +297,8 @@ ggml_openblas.o: ggml.c ggml.h
 	$(CC)  $(CFLAGS) $(FULLCFLAGS) $(OPENBLAS_FLAGS) -c $< -o $@
 ggml_failsafe.o: ggml.c ggml.h
 	$(CC)  $(CFLAGS) $(NONECFLAGS) -c $< -o $@
-ggml_openblas_noavx2.o: ggml.c ggml.h
-	$(CC)  $(CFLAGS) $(SIMPLECFLAGS) $(OPENBLAS_FLAGS) -c $< -o $@
+ggml_noavx2.o: ggml.c ggml.h
+	$(CC)  $(CFLAGS) $(SIMPLECFLAGS) -c $< -o $@
 ggml_clblast.o: ggml.c ggml.h
 	$(CC)  $(CFLAGS) $(FULLCFLAGS) $(CLBLAST_FLAGS) -c $< -o $@
 ggml_cublas.o: ggml.c ggml.h
@@ -298,6 +312,10 @@ k_quants_noavx2.o: k_quants.c k_quants.h ggml.h ggml-cuda.h
 k_quants_failsafe.o: k_quants.c k_quants.h ggml.h ggml-cuda.h
 	$(CC)  $(CFLAGS) $(NONECFLAGS) -c $< -o $@
 
+#there's no intrinsics or special gpu ops used here, so we can have a universal object
+ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
+	$(CC)  $(CFLAGS) -c $< -o $@
+
 #version 2 libs
 ggml_v2.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
 	$(CC)  $(CFLAGS) $(FULLCFLAGS) -c $< -o $@
@@ -305,8 +323,8 @@ ggml_v2_openblas.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
 	$(CC)  $(CFLAGS) $(FULLCFLAGS) $(OPENBLAS_FLAGS) -c $< -o $@
 ggml_v2_failsafe.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
 	$(CC)  $(CFLAGS) $(NONECFLAGS) -c $< -o $@
-ggml_v2_openblas_noavx2.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
-	$(CC)  $(CFLAGS) $(SIMPLECFLAGS) $(OPENBLAS_FLAGS) -c $< -o $@
+ggml_v2_noavx2.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
+	$(CC)  $(CFLAGS) $(SIMPLECFLAGS) -c $< -o $@
 ggml_v2_clblast.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
 	$(CC)  $(CFLAGS) $(FULLCFLAGS) $(CLBLAST_FLAGS) -c $< -o $@
 ggml_v2_cublas.o: otherarch/ggml_v2.c otherarch/ggml_v2.h
@@ -327,10 +345,12 @@ ggml_v2-opencl-legacy.o: otherarch/ggml_v2-opencl-legacy.c otherarch/ggml_v2-ope
 	$(CC) $(CFLAGS) -c $< -o $@
 
 # intermediate objects
-llama.o: llama.cpp ggml.h ggml-cuda.h llama.h llama-util.h
+llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h llama-util.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 common.o: examples/common.cpp examples/common.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
+console.o: examples/console.cpp examples/console.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
 grammar-parser.o: examples/grammar-parser.cpp examples/grammar-parser.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 expose.o: expose.cpp expose.h
@@ -348,37 +368,37 @@ gpttype_adapter_cublas.o: $(GPTTYPE_ADAPTER)
 	$(CXX) $(CXXFLAGS) $(CUBLAS_FLAGS) -c $< -o $@
 
 clean:
-	rm -vf *.o main quantize_llama quantize_gpt2 quantize_gptj quantize_neox quantize_mpt quantize-stats perplexity embedding benchmark-matmult save-load-state main.exe quantize_llama.exe quantize_gptj.exe quantize_gpt2.exe quantize_neox.exe quantize_mpt.exe koboldcpp.dll koboldcpp_openblas.dll koboldcpp_failsafe.dll koboldcpp_openblas_noavx2.dll koboldcpp_clblast.dll koboldcpp_cublas.dll koboldcpp.so koboldcpp_openblas.so koboldcpp_failsafe.so koboldcpp_openblas_noavx2.so koboldcpp_clblast.so koboldcpp_cublas.so
+	rm -vf *.o main quantize_llama quantize_gpt2 quantize_gptj quantize_neox quantize_mpt quantize-stats perplexity embedding benchmark-matmult save-load-state main.exe quantize_llama.exe quantize_gptj.exe quantize_gpt2.exe quantize_neox.exe quantize_mpt.exe koboldcpp.dll koboldcpp_openblas.dll koboldcpp_failsafe.dll koboldcpp_noavx2.dll koboldcpp_clblast.dll koboldcpp_cublas.dll koboldcpp.so koboldcpp_openblas.so koboldcpp_failsafe.so koboldcpp_noavx2.so koboldcpp_clblast.so koboldcpp_cublas.so
 
-main: examples/main/main.cpp build-info.h ggml.o k_quants.o llama.o common.o grammar-parser.o $(OBJS)
+main: examples/main/main.cpp build-info.h ggml.o k_quants.o ggml-alloc.o llama.o common.o console.o grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 	@echo
 	@echo '====  Run ./main -h for help.  ===='
 	@echo
 
 #generated libraries
-koboldcpp: ggml.o ggml_v2.o ggml_v1.o expose.o common.o gpttype_adapter.o k_quants.o $(OBJS)
+koboldcpp: ggml.o ggml_v2.o ggml_v1.o expose.o common.o gpttype_adapter.o k_quants.o ggml-alloc.o $(OBJS)
 	$(DEFAULT_BUILD)
-koboldcpp_openblas: ggml_openblas.o ggml_v2_openblas.o ggml_v1.o expose.o common.o gpttype_adapter.o k_quants.o $(OBJS)
+koboldcpp_openblas: ggml_openblas.o ggml_v2_openblas.o ggml_v1.o expose.o common.o gpttype_adapter.o k_quants.o ggml-alloc.o $(OBJS)
 	$(OPENBLAS_BUILD)
-koboldcpp_failsafe: ggml_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_failsafe.o k_quants_failsafe.o $(OBJS)
+koboldcpp_failsafe: ggml_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_failsafe.o k_quants_failsafe.o ggml-alloc.o $(OBJS)
 	$(FAILSAFE_BUILD)
-koboldcpp_openblas_noavx2: ggml_openblas_noavx2.o ggml_v2_openblas_noavx2.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_failsafe.o k_quants_noavx2.o $(OBJS)
-	$(OPENBLAS_NOAVX2_BUILD)
-koboldcpp_clblast: ggml_clblast.o ggml_v2_clblast.o ggml_v1.o expose.o common.o gpttype_adapter_clblast.o ggml-opencl.o ggml_v2-opencl.o ggml_v2-opencl-legacy.o k_quants.o $(OBJS)
+koboldcpp_noavx2: ggml_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_failsafe.o k_quants_noavx2.o ggml-alloc.o $(OBJS)
+	$(NOAVX2_BUILD)
+koboldcpp_clblast: ggml_clblast.o ggml_v2_clblast.o ggml_v1.o expose.o common.o gpttype_adapter_clblast.o ggml-opencl.o ggml_v2-opencl.o ggml_v2-opencl-legacy.o k_quants.o ggml-alloc.o $(OBJS)
 	$(CLBLAST_BUILD)
-koboldcpp_cublas: ggml_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o common.o gpttype_adapter_cublas.o k_quants.o $(CUBLAS_OBJS) $(OBJS)
+koboldcpp_cublas: ggml_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o common.o gpttype_adapter_cublas.o k_quants.o ggml-alloc.o $(CUBLAS_OBJS) $(OBJS)
 	$(CUBLAS_BUILD)
 
-quantize_llama: examples/quantize/quantize.cpp ggml.o llama.o k_quants.o
+quantize_llama: examples/quantize/quantize.cpp ggml.o llama.o k_quants.o ggml-alloc.o
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_gptj: ggml.o llama.o k_quants.o otherarch/tools/gptj_quantize.cpp otherarch/tools/common-ggml.cpp
+quantize_gptj: ggml.o llama.o k_quants.o ggml-alloc.o otherarch/tools/gptj_quantize.cpp otherarch/tools/common-ggml.cpp
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_gpt2: ggml.o llama.o k_quants.o otherarch/tools/gpt2_quantize.cpp otherarch/tools/common-ggml.cpp
+quantize_gpt2: ggml.o llama.o k_quants.o ggml-alloc.o otherarch/tools/gpt2_quantize.cpp otherarch/tools/common-ggml.cpp
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_neox: ggml.o llama.o k_quants.o otherarch/tools/neox_quantize.cpp otherarch/tools/common-ggml.cpp
+quantize_neox: ggml.o llama.o k_quants.o ggml-alloc.o otherarch/tools/neox_quantize.cpp otherarch/tools/common-ggml.cpp
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_mpt: ggml.o llama.o k_quants.o otherarch/tools/mpt_quantize.cpp otherarch/tools/common-ggml.cpp
+quantize_mpt: ggml.o llama.o k_quants.o ggml-alloc.o otherarch/tools/mpt_quantize.cpp otherarch/tools/common-ggml.cpp
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 
 
diff --git a/README.md b/README.md
index c1856b7f83666a8b9eae4fbd26748ca150f1fab9..4a1889bf9e46f62cccfd8fdc83d29aaec523a8c7 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,80 @@
----
-title: Koboldcpp
-sdk: docker
-colorFrom: blue
-colorTo: green
----
\ No newline at end of file
+# koboldcpp
+
+KoboldCpp is an easy-to-use AI text-generation software for GGML models. It's a single self contained distributable from Concedo, that builds off llama.cpp, and adds a versatile Kobold API endpoint, additional format support, backward compatibility, as well as a fancy UI with persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and everything Kobold and Kobold Lite have to offer.
+
+![Preview](media/preview.png)
+
+## Usage
+- **[Download the latest .exe release here](https://github.com/LostRuins/koboldcpp/releases/latest)** or clone the git repo.
+- Windows binaries are provided in the form of **koboldcpp.exe**, which is a pyinstaller wrapper for a few **.dll** files and **koboldcpp.py**. If you feel concerned, you may prefer to rebuild it yourself with the provided makefiles and scripts.
+- Weights are not included, you can use the official llama.cpp `quantize.exe` to generate them from your official weight files (or download them from other places such as [TheBloke's Huggingface](https://huggingface.co/TheBloke).
+- To run, execute **koboldcpp.exe** or drag and drop your quantized `ggml_model.bin` file onto the .exe, and then connect with Kobold or Kobold Lite. If you're not on windows, then run the script **KoboldCpp.py** after compiling the libraries.
+- Launching with no command line arguments displays a GUI containing a subset of configurable settings. Generally you dont have to change much besides the `Presets` and `GPU Layers`. Read the `--help` for more info about each settings.
+- By default, you can connect to http://localhost:5001
+- You can also run it using the command line `koboldcpp.exe [ggml_model.bin] [port]`. For info, please check `koboldcpp.exe --help`
+- Default context size to small? Try `--contextsize 3072` to 1.5x your context size! without much perplexity gain. Note that you'll have to increase the max context in the Kobold Lite UI as well (click and edit the number text field).
+- Big context too slow? Try the `--smartcontext` flag to reduce prompt processing frequency. Also, you can try to run with your GPU using CLBlast, with `--useclblast` flag for a speedup
+- Want even more speedup? Combine `--useclblast` with `--gpulayers` to offload entire layers to the GPU! **Much faster, but uses more VRAM**. Experiment to determine number of layers to offload, and reduce by a few if you run out of memory.
+- If you are having crashes or issues, you can try turning off BLAS with the `--noblas` flag. You can also try running in a non-avx2 compatibility mode with `--noavx2`. Lastly, you can try turning off mmap with `--nommap`.
+
+For more information, be sure to run the program with the `--help` flag.
+
+## OSX and Linux
+- You will have to compile your binaries from source. A makefile is provided, simply run `make`
+- If you want you can also link your own install of OpenBLAS manually with `make LLAMA_OPENBLAS=1`
+- Alternatively, if you want you can also link your own install of CLBlast manually with `make LLAMA_CLBLAST=1`, for this you will need to obtain and link OpenCL and CLBlast libraries.
+  - For Arch Linux: Install `cblas` `openblas` and `clblast`.
+  - For Debian: Install `libclblast-dev` and `libopenblas-dev`.
+- For a full featured build, do `make LLAMA_OPENBLAS=1 LLAMA_CLBLAST=1 LLAMA_CUBLAS=1`
+- After all binaries are built, you can run the python script with the command `koboldcpp.py [ggml_model.bin] [port]`
+- Note: Many OSX users have found that the using Accelerate is actually faster than OpenBLAS. To try, you may wish to run with `--noblas` and compare speeds.
+
+## Compiling on Windows
+- You're encouraged to use the .exe released, but if you want to compile your binaries from source at Windows, the easiest way is:
+  - Use the latest release of w64devkit (https://github.com/skeeto/w64devkit). Be sure to use the "vanilla one", not i686 or other different stuff. If you try they will conflit with the precompiled libs!
+  - Make sure you are using the w64devkit integrated terminal, then run 'make' at the KoboldCpp source folder. This will create the .dll files.
+  - If you want to generate the .exe file, make sure you have the python module PyInstaller installed with pip ('pip install PyInstaller').
+  - Run the script make_pyinstaller.bat at a regular terminal (or Windows Explorer).
+  - The koboldcpp.exe file will be at your dist folder.
+- If you wish to use your own version of the additional Windows libraries (OpenCL, CLBlast and OpenBLAS), you can do it with:
+  - OpenCL - tested with https://github.com/KhronosGroup/OpenCL-SDK . If you wish to compile it, follow the repository instructions. You will need vcpkg.
+  - CLBlast - tested with https://github.com/CNugteren/CLBlast . If you wish to compile it you will need to reference the OpenCL files. It will only generate the ".lib" file if you compile using MSVC.
+  - OpenBLAS - tested with https://github.com/xianyi/OpenBLAS .
+  - Move the respectives .lib files to the /lib folder of your project, overwriting the older files.
+  - Also, replace the existing versions of the corresponding .dll files located in the project directory root (e.g. libopenblas.dll).
+  - Make the KoboldCPP project using the instructions above.
+
+## Android (Termux) Alternative method
+- See https://github.com/ggerganov/llama.cpp/pull/1828/files
+
+## Using CuBLAS
+- If you're on Windows with an Nvidia GPU you can get CUDA support out of the box using the `--usecublas` flag, make sure you select the correct .exe with CUDA support.
+- You can attempt a CuBLAS build with `LLAMA_CUBLAS=1` or using the provided CMake file (best for visual studio users). If you use the CMake file to build, copy the `koboldcpp_cublas.dll` generated into the same directory as the `koboldcpp.py` file. If you are bundling executables, you may need to include CUDA dynamic libraries (such as `cublasLt64_11.dll` and `cublas64_11.dll`) in order for the executable to work correctly on a different PC.
+
+## Questions and Help
+- **First, please check out [The KoboldCpp FAQ and Knowledgebase](https://github.com/LostRuins/koboldcpp/wiki) which may already have answers to your questions! Also please search through past issues and discussions.**
+- If you cannot find an answer, open an issue on this github, or find us on the [KoboldAI Discord](https://koboldai.org/discord).
+
+## Considerations
+- For Windows: No installation, single file executable, (It Just Works)
+- Since v1.0.6, requires libopenblas, the prebuilt windows binaries are included in this repo. If not found, it will fall back to a mode without BLAS.
+- Since v1.15, requires CLBlast if enabled, the prebuilt windows binaries are included in this repo. If not found, it will fall back to a mode without CLBlast.
+- Since v1.33, you can set the context size to be above what the model supports officially. It does increases perplexity but should still work well below 4096 even on untuned models. (For GPT-NeoX, GPT-J, and LLAMA models) Customize this with `--ropeconfig`.
+- **I plan to keep backwards compatibility with ALL past llama.cpp AND alpaca.cpp models**. But you are also encouraged to reconvert/update your models if possible for best results.
+
+## License
+- The original GGML library and llama.cpp by ggerganov are licensed under the MIT License
+- However, Kobold Lite is licensed under the AGPL v3.0 License
+- The other files are also under the AGPL v3.0 License unless otherwise stated
+
+## Notes
+- Generation delay scales linearly with original prompt length. If OpenBLAS is enabled then prompt ingestion becomes about 2-3x faster. This is automatic on windows, but will require linking on OSX and Linux. CLBlast speeds this up even further, and `--gpulayers` + `--useclblast` more so.
+- I have heard of someone claiming a false AV positive report. The exe is a simple pyinstaller bundle that includes the necessary python scripts and dlls to run. If this still concerns you, you might wish to rebuild everything from source code using the makefile, and you can rebuild the exe yourself with pyinstaller by using `make_pyinstaller.bat`
+- Supported GGML models (Includes backward compatibility for older versions/legacy GGML models, though some newer features might be unavailable):
+  - LLAMA and LLAMA2 (LLaMA / Alpaca / GPT4All / Vicuna / Koala / Pygmalion 7B / Metharme 7B / WizardLM and many more)
+  - GPT-2 / Cerebras
+  - GPT-J
+  - RWKV
+  - GPT-NeoX / Pythia / StableLM / Dolly / RedPajama
+  - MPT models
+
diff --git a/convert.py b/convert.py
index ac99579c49eb2a27528fa0d1c811d41d214095f1..f3bf1798089ccd8d4c9f4db85deb313f60d8c175 100644
--- a/convert.py
+++ b/convert.py
@@ -133,7 +133,7 @@ TENSORS_SET = set(TENSORS_LIST)
 
 def find_n_mult(n_ff: int, n_embd: int) -> int:
     # hardcoded magic range
-    for n_mult in range(256, 1, -1):
+    for n_mult in range(8192, 1, -1):
         calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
         if calc_ff == n_ff:
             return n_mult
@@ -141,11 +141,12 @@ def find_n_mult(n_ff: int, n_embd: int) -> int:
 
 @dataclass
 class Params:
-    n_vocab: int
-    n_embd:  int
-    n_mult:  int
-    n_head:  int
-    n_layer: int
+    n_vocab:   int
+    n_embd:    int
+    n_mult:    int
+    n_head:    int
+    n_layer:   int
+    n_kv_head: Optional[int]  # This parameter is only used for Llama 2
 
     @staticmethod
     def guessed(model: 'LazyModel') -> 'Params':
@@ -167,11 +168,12 @@ class Params:
         n_head=n_embd // 128 # guessed
 
         return Params(
-            n_vocab = n_vocab,
-            n_embd  = n_embd,
-            n_mult  = 256,
-            n_head  = n_head,
-            n_layer = n_layer,
+            n_vocab   = n_vocab,
+            n_embd    = n_embd,
+            n_mult    = 256,
+            n_head    = n_head,
+            n_layer   = n_layer,
+            n_kv_head = None,
         )
 
     @staticmethod
@@ -183,15 +185,17 @@ class Params:
         n_head  = config["num_attention_heads"];
         n_layer = config["num_hidden_layers"];
         n_ff    = config["intermediate_size"];
+        n_kv_head = config.get("num_key_value_heads")
 
         n_mult = find_n_mult(n_ff, n_embd);
 
         return Params(
-            n_vocab = n_vocab,
-            n_embd  = n_embd,
-            n_mult  = n_mult,
-            n_head  = n_head,
-            n_layer = n_layer,
+            n_vocab   = n_vocab,
+            n_embd    = n_embd,
+            n_mult    = n_mult,
+            n_head    = n_head,
+            n_layer   = n_layer,
+            n_kv_head = n_kv_head,
         )
 
     # LLaMA v2 70B params.json
@@ -200,21 +204,22 @@ class Params:
     def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
         config = json.load(open(config_path))
 
-        n_vocab = config["vocab_size"];
-        n_embd  = config["dim"];
-        n_head  = config["n_heads"];
-        n_layer = config["n_layers"];
-        n_mult  = config["multiple_of"];
+        n_vocab   = config["vocab_size"];
+        n_embd    = config["dim"];
+        n_head    = config["n_heads"];
+        n_layer   = config["n_layers"];
+        n_mult    = config["multiple_of"];
 
         if n_vocab == -1:
             n_vocab = model["tok_embeddings.weight"].shape[0]
 
         return Params(
-            n_vocab = n_vocab,
-            n_embd  = n_embd,
-            n_mult  = n_mult,
-            n_head  = n_head,
-            n_layer = n_layer,
+            n_vocab   = n_vocab,
+            n_embd    = n_embd,
+            n_mult    = n_mult,
+            n_head    = n_head,
+            n_layer   = n_layer,
+            n_kv_head = None,
         )
 
     @staticmethod
@@ -317,10 +322,12 @@ class GGMLVocab:
 Vocab = Union[SentencePieceVocab, GGMLVocab]
 
 
-def permute(weights: NDArray, n_head: int) -> NDArray:
+def permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
+    if n_kv_head is not None and n_head != n_kv_head:
+        n_head //= n_kv_head
     return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                   .swapaxes(1, 2)
-                   .reshape(weights.shape))
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
 
 
 def dequantize_q4(qvalues_pack32: NDArray, scales: NDArray, addends: Optional[NDArray], g_idx: Optional[NDArray]) -> NDArray:
@@ -368,7 +375,7 @@ class Tensor(metaclass=ABCMeta):
     @abstractmethod
     def astype(self, data_type: DataType) -> 'Tensor': ...
     @abstractmethod
-    def permute(self, n_head: int) -> 'Tensor': ...
+    def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'Tensor': ...
     @abstractmethod
     def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
     @abstractmethod
@@ -406,8 +413,8 @@ class UnquantizedTensor(Tensor):
         r = self.ndarray.shape[0] // 3
         return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
 
-    def permute(self, n_head: int) -> 'UnquantizedTensor':
-        return UnquantizedTensor(permute(self.ndarray, n_head))
+    def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'UnquantizedTensor':
+        return UnquantizedTensor(permute(self.ndarray, n_head, n_kv_head))
 
 
 def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray:
@@ -455,26 +462,34 @@ class GGMLQuantizedTensor(Tensor):
     def to_ggml(self) -> 'GGMLQuantizedTensor':
         return self
 
-    def permute(self, n_head: int) -> 'GGMLQuantizedTensor':
-        return GGMLQuantizedTensor(permute(self.ndarray, n_head), self.shape, self.data_type)
+    def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'GGMLQuantizedTensor':
+        return GGMLQuantizedTensor(permute(self.ndarray, n_head, n_kv_head), self.shape, self.data_type)
+
+    def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor':
+        r = self.ndarray.shape[0] // 3
+        return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head))
 
+    def part(self, n_part: int) -> 'UnquantizedTensor':
+        r = self.ndarray.shape[0] // 3
+        return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
 
 GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor]
 
 
 class DeferredPermutedTensor(Tensor):
-    def __init__(self, base: Tensor, n_head: int) -> None:
+    def __init__(self, base: Tensor, n_head: int, n_kv_head: Optional[int] = None) -> None:
         self.base = base
         self.n_head = n_head
+        self.n_kv_head = n_kv_head
         self.data_type = self.base.data_type
 
     def astype(self, data_type: DataType) -> Tensor:
-        return self.base.astype(data_type).permute(self.n_head)
+        return self.base.astype(data_type).permute(self.n_head, self.n_kv_head)
 
     def to_ggml(self) -> GGMLCompatibleTensor:
-        return self.base.to_ggml().permute(self.n_head)
+        return self.base.to_ggml().permute(self.n_head, self.n_kv_head)
 
-    def permute(self, n_head: int) -> Tensor:
+    def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor:
         raise Exception("shouldn't permute twice")
 
 
@@ -566,8 +581,8 @@ class GPTQForLLaMaQuantizedTensor(Tensor):
         ret.data_type = QuantizedDataType(groupsize=new_groupsize, have_addends=True, have_g_idx=False)
         return ret
 
-    def permute(self, n_head: int) -> Tensor:
-        return DeferredPermutedTensor(self, n_head)
+    def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor:
+        return DeferredPermutedTensor(self, n_head, n_kv_head)
 
     def to_ggml(self) -> GGMLQuantizedTensor:
         # The output format looks like this:
@@ -698,10 +713,10 @@ def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus:
     return ModelPlus(model, paths, format, vocab)
 
 
-def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor:
+def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_kv_head: Optional[int] = None) -> LazyTensor:
     def load() -> Tensor:
-        return lazy_tensor.load().permute(n_head)
-    return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
+        return lazy_tensor.load().permute(n_head, n_kv_head)
+    return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_kv_head}) ' + lazy_tensor.description)
 
 def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
     def load() -> Tensor:
@@ -726,7 +741,7 @@ def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel:
     for i in itertools.count():
         if f"model.layers.{i}.self_attn.q_proj.weight" in model:
             out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head)
-            out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head)
+            out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_kv_head)
             out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
         elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
             out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head)
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 4b1f1cf44aaa160393bf7c75138546f53b9d920c..a7b26776ad355e10c32de98c29b497d8e68e0a85 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -13,6 +13,8 @@ set(TARGET common)
 add_library(${TARGET} OBJECT
     common.h
     common.cpp
+    console.h
+    console.cpp
     grammar-parser.h
     grammar-parser.cpp
     )
diff --git a/examples/common.cpp b/examples/common.cpp
index dd964c8a7481a32808781f8b10d96d8c16c124d8..4d3ba9bb23ab4779a67942fae22b187abf199dfd 100644
--- a/examples/common.cpp
+++ b/examples/common.cpp
@@ -25,7 +25,6 @@
 #else
 #include <sys/ioctl.h>
 #include <unistd.h>
-#include <wchar.h>
 #endif
 
 #if defined(_MSC_VER)
@@ -195,6 +194,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.rope_freq_scale = std::stof(argv[i]);
+        } else if (arg == "--rope-scale") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.rope_freq_scale = 1.0f/std::stof(argv[i]);
         } else if (arg == "--memory-f32") {
             params.memory_f16 = false;
         } else if (arg == "--top-p") {
@@ -329,6 +334,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             params.instruct = true;
         } else if (arg == "--multiline-input") {
             params.multiline_input = true;
+        } else if (arg == "--simple-io") {
+            params.simple_io = true;
         } else if (arg == "--color") {
             params.use_color = true;
         } else if (arg == "--mlock") {
@@ -352,7 +359,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
 #ifdef GGML_USE_CUBLAS
             params.main_gpu = std::stoi(argv[i]);
 #else
-      fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
+            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
 #endif
         } else if (arg == "--tensor-split" || arg == "-ts") {
             if (++i >= argc) {
@@ -376,13 +383,19 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 }
             }
 #else
-      fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
+            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
+#endif // GGML_USE_CUBLAS
+        } else if (arg == "--mul-mat-q" || arg == "-mmq") {
+#ifdef GGML_USE_CUBLAS
+            params.mul_mat_q = true;
+#else
+            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n");
 #endif // GGML_USE_CUBLAS
         } else if (arg == "--low-vram" || arg == "-lv") {
 #ifdef GGML_USE_CUBLAS
             params.low_vram = true;
 #else
-      fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
+            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
 #endif // GGML_USE_CUBLAS
         } else if (arg == "--no-mmap") {
             params.use_mmap = false;
@@ -402,8 +415,14 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             params.antiprompt.push_back(argv[i]);
         } else if (arg == "--perplexity") {
             params.perplexity = true;
-        } else if (arg == "--perplexity-lines") {
-            params.perplexity_lines = true;
+        } else if (arg == "--hellaswag") {
+            params.hellaswag = true;
+        } else if (arg == "--hellaswag-tasks") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.hellaswag_tasks = std::stoi(argv[i]);
         } else if (arg == "--ignore-eos") {
             params.logit_bias[llama_token_eos()] = -INFINITY;
         } else if (arg == "--no-penalize-nl") {
@@ -551,16 +570,18 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stdout, "  --cfg-negative-prompt PROMPT \n");
     fprintf(stdout, "                        negative prompt to use for guidance. (default: empty)\n");
     fprintf(stdout, "  --cfg-scale N         strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
-    fprintf(stdout, "  --rope-freq-base N    RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
-    fprintf(stdout, "  --rope-freq-scale N   RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
+    fprintf(stdout, "  --rope-scale N        RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale);
+    fprintf(stdout, "  --rope-freq-base N    RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base);
+    fprintf(stdout, "  --rope-freq-scale N   RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale);
     fprintf(stdout, "  --ignore-eos          ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
     fprintf(stdout, "  --no-penalize-nl      do not penalize newline token\n");
     fprintf(stdout, "  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
     fprintf(stdout, "                        not recommended: doubles context memory required and no measurable increase in quality\n");
     fprintf(stdout, "  --temp N              temperature (default: %.1f)\n", (double)params.temp);
     fprintf(stdout, "  --perplexity          compute perplexity over each ctx window of the prompt\n");
-    fprintf(stdout, "  --perplexity-lines    compute perplexity over each line of the prompt\n");
-    fprintf(stdout, "  --keep                number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
+    fprintf(stdout, "  --hellaswag           compute HellaSwag score over random tasks from datafile supplied with -f\n");
+    fprintf(stdout, "  --hellaswag-tasks N   number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
+    fprintf(stdout, "  --keep N              number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
     fprintf(stdout, "  --chunks N            max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
     if (llama_mlock_supported()) {
         fprintf(stdout, "  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
@@ -578,10 +599,14 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stdout, "                        how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
     fprintf(stdout, "  -mg i, --main-gpu i   the GPU to use for scratch and small tensors\n" );
     fprintf(stdout, "  -lv, --low-vram       don't allocate VRAM scratch buffer\n" );
+    fprintf(stdout, "  -mmq, --mul-mat-q     use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
+    fprintf(stdout, "                        Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
+    fprintf(stdout, "                        is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
 #endif
     fprintf(stdout, "  --mtest               compute maximum memory usage\n");
     fprintf(stdout, "  --export              export the computation graph to 'llama.ggml'\n");
     fprintf(stdout, "  --verbose-prompt      print prompt before generation\n");
+    fprintf(stderr, "  --simple-io           use basic IO for better compatibility in subprocesses and limited consoles\n");
     fprintf(stdout, "  --lora FNAME          apply LoRA adapter (implies --no-mmap)\n");
     fprintf(stdout, "  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
     fprintf(stdout, "  -m FNAME, --model FNAME\n");
@@ -630,6 +655,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     lparams.main_gpu        = params.main_gpu;
     lparams.tensor_split    = params.tensor_split;
     lparams.low_vram        = params.low_vram;
+    lparams.mul_mat_q       = params.mul_mat_q;
     lparams.seed            = params.seed;
     lparams.f16_kv          = params.memory_f16;
     lparams.use_mmap        = params.use_mmap;
@@ -673,376 +699,3 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
 
     return std::make_tuple(model, lctx);
 }
-
-void console_init(console_state & con_st) {
-#if defined(_WIN32)
-    // Windows-specific console initialization
-    DWORD dwMode = 0;
-    con_st.hConsole = GetStdHandle(STD_OUTPUT_HANDLE);
-    if (con_st.hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(con_st.hConsole, &dwMode)) {
-        con_st.hConsole = GetStdHandle(STD_ERROR_HANDLE);
-        if (con_st.hConsole != INVALID_HANDLE_VALUE && (!GetConsoleMode(con_st.hConsole, &dwMode))) {
-            con_st.hConsole = NULL;
-        }
-    }
-    if (con_st.hConsole) {
-        // Enable ANSI colors on Windows 10+
-        if (con_st.use_color && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING)) {
-            SetConsoleMode(con_st.hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING);
-        }
-        // Set console output codepage to UTF8
-        SetConsoleOutputCP(CP_UTF8);
-    }
-    HANDLE hConIn = GetStdHandle(STD_INPUT_HANDLE);
-    if (hConIn != INVALID_HANDLE_VALUE && GetConsoleMode(hConIn, &dwMode)) {
-        // Set console input codepage to UTF16
-        _setmode(_fileno(stdin), _O_WTEXT);
-
-        // Turn off ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT)
-        dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT);
-        SetConsoleMode(hConIn, dwMode);
-    }
-#else
-    // POSIX-specific console initialization
-    struct termios new_termios;
-    tcgetattr(STDIN_FILENO, &con_st.prev_state);
-    new_termios = con_st.prev_state;
-    new_termios.c_lflag &= ~(ICANON | ECHO);
-    new_termios.c_cc[VMIN] = 1;
-    new_termios.c_cc[VTIME] = 0;
-    tcsetattr(STDIN_FILENO, TCSANOW, &new_termios);
-
-    con_st.tty = fopen("/dev/tty", "w+");
-    if (con_st.tty != nullptr) {
-        con_st.out = con_st.tty;
-    }
-
-    setlocale(LC_ALL, "");
-#endif
-}
-
-void console_cleanup(console_state & con_st) {
-    // Reset console color
-    console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
-
-#if !defined(_WIN32)
-    if (con_st.tty != nullptr) {
-        con_st.out = stdout;
-        fclose(con_st.tty);
-        con_st.tty = nullptr;
-    }
-    // Restore the terminal settings on POSIX systems
-    tcsetattr(STDIN_FILENO, TCSANOW, &con_st.prev_state);
-#endif
-}
-
-/* Keep track of current color of output, and emit ANSI code if it changes. */
-void console_set_color(console_state & con_st, console_color_t color) {
-    if (con_st.use_color && con_st.color != color) {
-        fflush(stdout);
-        switch(color) {
-            case CONSOLE_COLOR_DEFAULT:
-                fprintf(con_st.out, ANSI_COLOR_RESET);
-                break;
-            case CONSOLE_COLOR_PROMPT:
-                fprintf(con_st.out, ANSI_COLOR_YELLOW);
-                break;
-            case CONSOLE_COLOR_USER_INPUT:
-                fprintf(con_st.out, ANSI_BOLD ANSI_COLOR_GREEN);
-                break;
-            case CONSOLE_COLOR_ERROR:
-                fprintf(con_st.out, ANSI_BOLD ANSI_COLOR_RED);
-                break;
-        }
-        con_st.color = color;
-        fflush(con_st.out);
-    }
-}
-
-char32_t getchar32() {
-#if defined(_WIN32)
-    HANDLE hConsole = GetStdHandle(STD_INPUT_HANDLE);
-    wchar_t high_surrogate = 0;
-
-    while (true) {
-        INPUT_RECORD record;
-        DWORD count;
-        if (!ReadConsoleInputW(hConsole, &record, 1, &count) || count == 0) {
-            return WEOF;
-        }
-
-        if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) {
-            wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar;
-            if (wc == 0) {
-                continue;
-            }
-
-            if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
-                high_surrogate = wc;
-                continue;
-            } else if ((wc >= 0xDC00) && (wc <= 0xDFFF)) { // Check if wc is a low surrogate
-                if (high_surrogate != 0) { // Check if we have a high surrogate
-                    return ((high_surrogate - 0xD800) << 10) + (wc - 0xDC00) + 0x10000;
-                }
-            }
-
-            high_surrogate = 0; // Reset the high surrogate
-            return static_cast<char32_t>(wc);
-        }
-    }
-#else
-    wchar_t wc = getwchar();
-    if (static_cast<wint_t>(wc) == WEOF) {
-        return WEOF;
-    }
-
-#if WCHAR_MAX == 0xFFFF
-    if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
-        wchar_t low_surrogate = getwchar();
-        if ((low_surrogate >= 0xDC00) && (low_surrogate <= 0xDFFF)) { // Check if the next wchar is a low surrogate
-            return (static_cast<char32_t>(wc & 0x03FF) << 10) + (low_surrogate & 0x03FF) + 0x10000;
-        }
-    }
-    if ((wc >= 0xD800) && (wc <= 0xDFFF)) { // Invalid surrogate pair
-        return 0xFFFD; // Return the replacement character U+FFFD
-    }
-#endif
-
-    return static_cast<char32_t>(wc);
-#endif
-}
-
-void pop_cursor(console_state & con_st) {
-#if defined(_WIN32)
-    if (con_st.hConsole != NULL) {
-        CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
-        GetConsoleScreenBufferInfo(con_st.hConsole, &bufferInfo);
-
-        COORD newCursorPosition = bufferInfo.dwCursorPosition;
-        if (newCursorPosition.X == 0) {
-            newCursorPosition.X = bufferInfo.dwSize.X - 1;
-            newCursorPosition.Y -= 1;
-        } else {
-            newCursorPosition.X -= 1;
-        }
-
-        SetConsoleCursorPosition(con_st.hConsole, newCursorPosition);
-        return;
-    }
-#endif
-    putc('\b', con_st.out);
-}
-
-int estimateWidth(char32_t codepoint) {
-#if defined(_WIN32)
-    return 1;
-#else
-    return wcwidth(codepoint);
-#endif
-}
-
-int put_codepoint(console_state & con_st, const char* utf8_codepoint, size_t length, int expectedWidth) {
-#if defined(_WIN32)
-    CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
-    if (!GetConsoleScreenBufferInfo(con_st.hConsole, &bufferInfo)) {
-        // go with the default
-        return expectedWidth;
-    }
-    COORD initialPosition = bufferInfo.dwCursorPosition;
-    DWORD nNumberOfChars = length;
-    WriteConsole(con_st.hConsole, utf8_codepoint, nNumberOfChars, &nNumberOfChars, NULL);
-
-    CONSOLE_SCREEN_BUFFER_INFO newBufferInfo;
-    GetConsoleScreenBufferInfo(con_st.hConsole, &newBufferInfo);
-
-    // Figure out our real position if we're in the last column
-    if (utf8_codepoint[0] != 0x09 && initialPosition.X == newBufferInfo.dwSize.X - 1) {
-        DWORD nNumberOfChars;
-        WriteConsole(con_st.hConsole, &" \b", 2, &nNumberOfChars, NULL);
-        GetConsoleScreenBufferInfo(con_st.hConsole, &newBufferInfo);
-    }
-
-    int width = newBufferInfo.dwCursorPosition.X - initialPosition.X;
-    if (width < 0) {
-        width += newBufferInfo.dwSize.X;
-    }
-    return width;
-#else
-    // we can trust expectedWidth if we've got one
-    if (expectedWidth >= 0 || con_st.tty == nullptr) {
-        fwrite(utf8_codepoint, length, 1, con_st.out);
-        return expectedWidth;
-    }
-
-    fputs("\033[6n", con_st.tty); // Query cursor position
-    int x1, x2, y1, y2;
-    int results = 0;
-    results = fscanf(con_st.tty, "\033[%d;%dR", &y1, &x1);
-
-    fwrite(utf8_codepoint, length, 1, con_st.tty);
-
-    fputs("\033[6n", con_st.tty); // Query cursor position
-    results += fscanf(con_st.tty, "\033[%d;%dR", &y2, &x2);
-
-    if (results != 4) {
-        return expectedWidth;
-    }
-
-    int width = x2 - x1;
-    if (width < 0) {
-        // Calculate the width considering text wrapping
-        struct winsize w;
-        ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);
-        width += w.ws_col;
-    }
-    return width;
-#endif
-}
-
-void replace_last(console_state & con_st, char ch) {
-#if defined(_WIN32)
-    pop_cursor(con_st);
-    put_codepoint(con_st, &ch, 1, 1);
-#else
-    fprintf(con_st.out, "\b%c", ch);
-#endif
-}
-
-void append_utf8(char32_t ch, std::string & out) {
-    if (ch <= 0x7F) {
-        out.push_back(static_cast<unsigned char>(ch));
-    } else if (ch <= 0x7FF) {
-        out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F)));
-        out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
-    } else if (ch <= 0xFFFF) {
-        out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F)));
-        out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
-        out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
-    } else if (ch <= 0x10FFFF) {
-        out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07)));
-        out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F)));
-        out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
-        out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
-    } else {
-        // Invalid Unicode code point
-    }
-}
-
-// Helper function to remove the last UTF-8 character from a string
-void pop_back_utf8_char(std::string & line) {
-    if (line.empty()) {
-        return;
-    }
-
-    size_t pos = line.length() - 1;
-
-    // Find the start of the last UTF-8 character (checking up to 4 bytes back)
-    for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) {
-        if ((line[pos] & 0xC0) != 0x80) break; // Found the start of the character
-    }
-    line.erase(pos);
-}
-
-bool console_readline(console_state & con_st, std::string & line) {
-    console_set_color(con_st, CONSOLE_COLOR_USER_INPUT);
-    if (con_st.out != stdout) {
-        fflush(stdout);
-    }
-
-    line.clear();
-    std::vector<int> widths;
-    bool is_special_char = false;
-    bool end_of_stream = false;
-
-    char32_t input_char;
-    while (true) {
-        fflush(con_st.out); // Ensure all output is displayed before waiting for input
-        input_char = getchar32();
-
-        if (input_char == '\r' || input_char == '\n') {
-            break;
-        }
-
-        if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) {
-            end_of_stream = true;
-            break;
-        }
-
-        if (is_special_char) {
-            console_set_color(con_st, CONSOLE_COLOR_USER_INPUT);
-            replace_last(con_st, line.back());
-            is_special_char = false;
-        }
-
-        if (input_char == '\033') { // Escape sequence
-            char32_t code = getchar32();
-            if (code == '[' || code == 0x1B) {
-                // Discard the rest of the escape sequence
-                while ((code = getchar32()) != (char32_t) WEOF) {
-                    if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') {
-                        break;
-                    }
-                }
-            }
-        } else if (input_char == 0x08 || input_char == 0x7F) { // Backspace
-            if (!widths.empty()) {
-                int count;
-                do {
-                    count = widths.back();
-                    widths.pop_back();
-                    // Move cursor back, print space, and move cursor back again
-                    for (int i = 0; i < count; i++) {
-                        replace_last(con_st, ' ');
-                        pop_cursor(con_st);
-                    }
-                    pop_back_utf8_char(line);
-                } while (count == 0 && !widths.empty());
-            }
-        } else {
-            int offset = line.length();
-            append_utf8(input_char, line);
-            int width = put_codepoint(con_st, line.c_str() + offset, line.length() - offset, estimateWidth(input_char));
-            if (width < 0) {
-                width = 0;
-            }
-            widths.push_back(width);
-        }
-
-        if (!line.empty() && (line.back() == '\\' || line.back() == '/')) {
-            console_set_color(con_st, CONSOLE_COLOR_PROMPT);
-            replace_last(con_st, line.back());
-            is_special_char = true;
-        }
-    }
-
-    bool has_more = con_st.multiline_input;
-    if (is_special_char) {
-        replace_last(con_st, ' ');
-        pop_cursor(con_st);
-
-        char last = line.back();
-        line.pop_back();
-        if (last == '\\') {
-            line += '\n';
-            fputc('\n', con_st.out);
-            has_more = !has_more;
-        } else {
-            // llama will just eat the single space, it won't act as a space
-            if (line.length() == 1 && line.back() == ' ') {
-                line.clear();
-                pop_cursor(con_st);
-            }
-            has_more = false;
-        }
-    } else {
-        if (end_of_stream) {
-            has_more = false;
-        } else {
-            line += '\n';
-            fputc('\n', con_st.out);
-        }
-    }
-
-    fflush(con_st.out);
-    return has_more;
-}
diff --git a/examples/common.h b/examples/common.h
index 672dcf77c8f992166ebeba2b5a7e630ac59172b9..375bc0a3db416b9bd4801d13c3924051feb7aa2d 100644
--- a/examples/common.h
+++ b/examples/common.h
@@ -11,11 +11,6 @@
 #include <unordered_map>
 #include <tuple>
 
-#if !defined (_WIN32)
-#include <stdio.h>
-#include <termios.h>
-#endif
-
 //
 // CLI argument parsing
 //
@@ -70,7 +65,11 @@ struct gpt_params {
     std::string lora_adapter = "";  // lora adapter path
     std::string lora_base    = "";  // base model path for the lora adapter
 
-    bool low_vram          = false;   // if true, reduce VRAM usage at the cost of performance
+    bool hellaswag         = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
+    size_t hellaswag_tasks = 400;   // number of tasks to use when computing the HellaSwag score
+
+    bool low_vram          = false; // if true, reduce VRAM usage at the cost of performance
+    bool mul_mat_q         = false; // if true, use experimental mul_mat_q kernels
     bool memory_f16        = true;  // use f16 instead of f32 for memory kv
     bool random_prompt     = false; // do not randomize prompt if none provided
     bool use_color         = false; // use color to distinguish generations and inputs
@@ -81,12 +80,12 @@ struct gpt_params {
     bool embedding         = false; // get only sentence embedding
     bool interactive_first = false; // wait for user input immediately
     bool multiline_input   = false; // reverse the usage of `\`
+    bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
     bool instruct          = false; // instruction mode (used for Alpaca models)
     bool penalize_nl       = true;  // consider newlines as a repeatable token
     bool perplexity        = false; // compute perplexity over the prompt
-    bool perplexity_lines  = false; // compute perplexity over each line of the prompt
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
     bool mem_test          = false; // compute maximum memory usage
@@ -113,42 +112,3 @@ std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::s
 
 std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(const gpt_params & params);
 struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
-
-//
-// Console utils
-//
-
-#define ANSI_COLOR_RED     "\x1b[31m"
-#define ANSI_COLOR_GREEN   "\x1b[32m"
-#define ANSI_COLOR_YELLOW  "\x1b[33m"
-#define ANSI_COLOR_BLUE    "\x1b[34m"
-#define ANSI_COLOR_MAGENTA "\x1b[35m"
-#define ANSI_COLOR_CYAN    "\x1b[36m"
-#define ANSI_COLOR_RESET   "\x1b[0m"
-#define ANSI_BOLD          "\x1b[1m"
-
-enum console_color_t {
-    CONSOLE_COLOR_DEFAULT=0,
-    CONSOLE_COLOR_PROMPT,
-    CONSOLE_COLOR_USER_INPUT,
-    CONSOLE_COLOR_ERROR
-};
-
-struct console_state {
-    bool multiline_input = false;
-    bool use_color = false;
-    console_color_t color = CONSOLE_COLOR_DEFAULT;
-
-    FILE* out = stdout;
-#if defined (_WIN32)
-    void* hConsole;
-#else
-    FILE* tty = nullptr;
-    termios prev_state;
-#endif
-};
-
-void console_init(console_state & con_st);
-void console_cleanup(console_state & con_st);
-void console_set_color(console_state & con_st, console_color_t color);
-bool console_readline(console_state & con_st, std::string & line);
diff --git a/examples/console.cpp b/examples/console.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..8966b107f079790081ceaa7fa9786d81a9eec322
--- /dev/null
+++ b/examples/console.cpp
@@ -0,0 +1,496 @@
+#include "console.h"
+#include <vector>
+#include <iostream>
+
+#if defined(_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#include <fcntl.h>
+#include <io.h>
+#else
+#include <climits>
+#include <sys/ioctl.h>
+#include <unistd.h>
+#include <wchar.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <signal.h>
+#include <termios.h>
+#endif
+
+#define ANSI_COLOR_RED     "\x1b[31m"
+#define ANSI_COLOR_GREEN   "\x1b[32m"
+#define ANSI_COLOR_YELLOW  "\x1b[33m"
+#define ANSI_COLOR_BLUE    "\x1b[34m"
+#define ANSI_COLOR_MAGENTA "\x1b[35m"
+#define ANSI_COLOR_CYAN    "\x1b[36m"
+#define ANSI_COLOR_RESET   "\x1b[0m"
+#define ANSI_BOLD          "\x1b[1m"
+
+namespace console {
+
+    //
+    // Console state
+    //
+
+    static bool      advanced_display = false;
+    static bool      simple_io        = true;
+    static display_t current_display  = reset;
+
+    static FILE*     out              = stdout;
+
+#if defined (_WIN32)
+    static void*     hConsole;
+#else
+    static FILE*     tty              = nullptr;
+    static termios   initial_state;
+#endif
+
+    //
+    // Init and cleanup
+    //
+
+    void init(bool use_simple_io, bool use_advanced_display) {
+        advanced_display = use_advanced_display;
+        simple_io = use_simple_io;
+#if defined(_WIN32)
+        // Windows-specific console initialization
+        DWORD dwMode = 0;
+        hConsole = GetStdHandle(STD_OUTPUT_HANDLE);
+        if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) {
+            hConsole = GetStdHandle(STD_ERROR_HANDLE);
+            if (hConsole != INVALID_HANDLE_VALUE && (!GetConsoleMode(hConsole, &dwMode))) {
+                hConsole = nullptr;
+                simple_io = true;
+            }
+        }
+        if (hConsole) {
+            // Enable ANSI colors on Windows 10+
+            if (advanced_display && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING)) {
+                SetConsoleMode(hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING);
+            }
+            // Set console output codepage to UTF8
+            SetConsoleOutputCP(CP_UTF8);
+        }
+        HANDLE hConIn = GetStdHandle(STD_INPUT_HANDLE);
+        if (hConIn != INVALID_HANDLE_VALUE && GetConsoleMode(hConIn, &dwMode)) {
+            // Set console input codepage to UTF16
+            _setmode(_fileno(stdin), _O_WTEXT);
+
+            // Set ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT)
+            if (simple_io) {
+                dwMode |= ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT;
+            } else {
+                dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT);
+            }
+            if (!SetConsoleMode(hConIn, dwMode)) {
+                simple_io = true;
+            }
+        }
+#else
+        // POSIX-specific console initialization
+        if (!simple_io) {
+            struct termios new_termios;
+            tcgetattr(STDIN_FILENO, &initial_state);
+            new_termios = initial_state;
+            new_termios.c_lflag &= ~(ICANON | ECHO);
+            new_termios.c_cc[VMIN] = 1;
+            new_termios.c_cc[VTIME] = 0;
+            tcsetattr(STDIN_FILENO, TCSANOW, &new_termios);
+
+            tty = fopen("/dev/tty", "w+");
+            if (tty != nullptr) {
+                out = tty;
+            }
+        }
+
+        setlocale(LC_ALL, "");
+#endif
+    }
+
+    void cleanup() {
+        // Reset console display
+        set_display(reset);
+
+#if !defined(_WIN32)
+        // Restore settings on POSIX systems
+        if (!simple_io) {
+            if (tty != nullptr) {
+                out = stdout;
+                fclose(tty);
+                tty = nullptr;
+            }
+            tcsetattr(STDIN_FILENO, TCSANOW, &initial_state);
+        }
+#endif
+    }
+
+    //
+    // Display and IO
+    //
+
+    // Keep track of current display and only emit ANSI code if it changes
+    void set_display(display_t display) {
+        if (advanced_display && current_display != display) {
+            fflush(stdout);
+            switch(display) {
+                case reset:
+                    fprintf(out, ANSI_COLOR_RESET);
+                    break;
+                case prompt:
+                    fprintf(out, ANSI_COLOR_YELLOW);
+                    break;
+                case user_input:
+                    fprintf(out, ANSI_BOLD ANSI_COLOR_GREEN);
+                    break;
+                case error:
+                    fprintf(out, ANSI_BOLD ANSI_COLOR_RED);
+            }
+            current_display = display;
+            fflush(out);
+        }
+    }
+
+    char32_t getchar32() {
+#if defined(_WIN32)
+        HANDLE hConsole = GetStdHandle(STD_INPUT_HANDLE);
+        wchar_t high_surrogate = 0;
+
+        while (true) {
+            INPUT_RECORD record;
+            DWORD count;
+            if (!ReadConsoleInputW(hConsole, &record, 1, &count) || count == 0) {
+                return WEOF;
+            }
+
+            if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) {
+                wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar;
+                if (wc == 0) {
+                    continue;
+                }
+
+                if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
+                    high_surrogate = wc;
+                    continue;
+                }
+                if ((wc >= 0xDC00) && (wc <= 0xDFFF)) { // Check if wc is a low surrogate
+                    if (high_surrogate != 0) { // Check if we have a high surrogate
+                        return ((high_surrogate - 0xD800) << 10) + (wc - 0xDC00) + 0x10000;
+                    }
+                }
+
+                high_surrogate = 0; // Reset the high surrogate
+                return static_cast<char32_t>(wc);
+            }
+        }
+#else
+        wchar_t wc = getwchar();
+        if (static_cast<wint_t>(wc) == WEOF) {
+            return WEOF;
+        }
+
+#if WCHAR_MAX == 0xFFFF
+        if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
+            wchar_t low_surrogate = getwchar();
+            if ((low_surrogate >= 0xDC00) && (low_surrogate <= 0xDFFF)) { // Check if the next wchar is a low surrogate
+                return (static_cast<char32_t>(wc & 0x03FF) << 10) + (low_surrogate & 0x03FF) + 0x10000;
+            }
+        }
+        if ((wc >= 0xD800) && (wc <= 0xDFFF)) { // Invalid surrogate pair
+            return 0xFFFD; // Return the replacement character U+FFFD
+        }
+#endif
+
+        return static_cast<char32_t>(wc);
+#endif
+    }
+
+    void pop_cursor() {
+#if defined(_WIN32)
+        if (hConsole != NULL) {
+            CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
+            GetConsoleScreenBufferInfo(hConsole, &bufferInfo);
+
+            COORD newCursorPosition = bufferInfo.dwCursorPosition;
+            if (newCursorPosition.X == 0) {
+                newCursorPosition.X = bufferInfo.dwSize.X - 1;
+                newCursorPosition.Y -= 1;
+            } else {
+                newCursorPosition.X -= 1;
+            }
+
+            SetConsoleCursorPosition(hConsole, newCursorPosition);
+            return;
+        }
+#endif
+        putc('\b', out);
+    }
+
+    int estimateWidth(char32_t codepoint) {
+#if defined(_WIN32)
+        return 1;
+#else
+        return wcwidth(codepoint);
+#endif
+    }
+
+    int put_codepoint(const char* utf8_codepoint, size_t length, int expectedWidth) {
+#if defined(_WIN32)
+        CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
+        if (!GetConsoleScreenBufferInfo(hConsole, &bufferInfo)) {
+            // go with the default
+            return expectedWidth;
+        }
+        COORD initialPosition = bufferInfo.dwCursorPosition;
+        DWORD nNumberOfChars = length;
+        WriteConsole(hConsole, utf8_codepoint, nNumberOfChars, &nNumberOfChars, NULL);
+
+        CONSOLE_SCREEN_BUFFER_INFO newBufferInfo;
+        GetConsoleScreenBufferInfo(hConsole, &newBufferInfo);
+
+        // Figure out our real position if we're in the last column
+        if (utf8_codepoint[0] != 0x09 && initialPosition.X == newBufferInfo.dwSize.X - 1) {
+            DWORD nNumberOfChars;
+            WriteConsole(hConsole, &" \b", 2, &nNumberOfChars, NULL);
+            GetConsoleScreenBufferInfo(hConsole, &newBufferInfo);
+        }
+
+        int width = newBufferInfo.dwCursorPosition.X - initialPosition.X;
+        if (width < 0) {
+            width += newBufferInfo.dwSize.X;
+        }
+        return width;
+#else
+        // We can trust expectedWidth if we've got one
+        if (expectedWidth >= 0 || tty == nullptr) {
+            fwrite(utf8_codepoint, length, 1, out);
+            return expectedWidth;
+        }
+
+        fputs("\033[6n", tty); // Query cursor position
+        int x1;
+        int y1;
+        int x2;
+        int y2;
+        int results = 0;
+        results = fscanf(tty, "\033[%d;%dR", &y1, &x1);
+
+        fwrite(utf8_codepoint, length, 1, tty);
+
+        fputs("\033[6n", tty); // Query cursor position
+        results += fscanf(tty, "\033[%d;%dR", &y2, &x2);
+
+        if (results != 4) {
+            return expectedWidth;
+        }
+
+        int width = x2 - x1;
+        if (width < 0) {
+            // Calculate the width considering text wrapping
+            struct winsize w;
+            ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);
+            width += w.ws_col;
+        }
+        return width;
+#endif
+    }
+
+    void replace_last(char ch) {
+#if defined(_WIN32)
+        pop_cursor();
+        put_codepoint(&ch, 1, 1);
+#else
+        fprintf(out, "\b%c", ch);
+#endif
+    }
+
+    void append_utf8(char32_t ch, std::string & out) {
+        if (ch <= 0x7F) {
+            out.push_back(static_cast<unsigned char>(ch));
+        } else if (ch <= 0x7FF) {
+            out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F)));
+            out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
+        } else if (ch <= 0xFFFF) {
+            out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F)));
+            out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
+            out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
+        } else if (ch <= 0x10FFFF) {
+            out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07)));
+            out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F)));
+            out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
+            out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
+        } else {
+            // Invalid Unicode code point
+        }
+    }
+
+    // Helper function to remove the last UTF-8 character from a string
+    void pop_back_utf8_char(std::string & line) {
+        if (line.empty()) {
+            return;
+        }
+
+        size_t pos = line.length() - 1;
+
+        // Find the start of the last UTF-8 character (checking up to 4 bytes back)
+        for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) {
+            if ((line[pos] & 0xC0) != 0x80) {
+                break; // Found the start of the character
+            }
+        }
+        line.erase(pos);
+    }
+
+    bool readline_advanced(std::string & line, bool multiline_input) {
+        if (out != stdout) {
+            fflush(stdout);
+        }
+
+        line.clear();
+        std::vector<int> widths;
+        bool is_special_char = false;
+        bool end_of_stream = false;
+
+        char32_t input_char;
+        while (true) {
+            fflush(out); // Ensure all output is displayed before waiting for input
+            input_char = getchar32();
+
+            if (input_char == '\r' || input_char == '\n') {
+                break;
+            }
+
+            if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) {
+                end_of_stream = true;
+                break;
+            }
+
+            if (is_special_char) {
+                set_display(user_input);
+                replace_last(line.back());
+                is_special_char = false;
+            }
+
+            if (input_char == '\033') { // Escape sequence
+                char32_t code = getchar32();
+                if (code == '[' || code == 0x1B) {
+                    // Discard the rest of the escape sequence
+                    while ((code = getchar32()) != (char32_t) WEOF) {
+                        if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') {
+                            break;
+                        }
+                    }
+                }
+            } else if (input_char == 0x08 || input_char == 0x7F) { // Backspace
+                if (!widths.empty()) {
+                    int count;
+                    do {
+                        count = widths.back();
+                        widths.pop_back();
+                        // Move cursor back, print space, and move cursor back again
+                        for (int i = 0; i < count; i++) {
+                            replace_last(' ');
+                            pop_cursor();
+                        }
+                        pop_back_utf8_char(line);
+                    } while (count == 0 && !widths.empty());
+                }
+            } else {
+                int offset = line.length();
+                append_utf8(input_char, line);
+                int width = put_codepoint(line.c_str() + offset, line.length() - offset, estimateWidth(input_char));
+                if (width < 0) {
+                    width = 0;
+                }
+                widths.push_back(width);
+            }
+
+            if (!line.empty() && (line.back() == '\\' || line.back() == '/')) {
+                set_display(prompt);
+                replace_last(line.back());
+                is_special_char = true;
+            }
+        }
+
+        bool has_more = multiline_input;
+        if (is_special_char) {
+            replace_last(' ');
+            pop_cursor();
+
+            char last = line.back();
+            line.pop_back();
+            if (last == '\\') {
+                line += '\n';
+                fputc('\n', out);
+                has_more = !has_more;
+            } else {
+                // llama will just eat the single space, it won't act as a space
+                if (line.length() == 1 && line.back() == ' ') {
+                    line.clear();
+                    pop_cursor();
+                }
+                has_more = false;
+            }
+        } else {
+            if (end_of_stream) {
+                has_more = false;
+            } else {
+                line += '\n';
+                fputc('\n', out);
+            }
+        }
+
+        fflush(out);
+        return has_more;
+    }
+
+    bool readline_simple(std::string & line, bool multiline_input) {
+#if defined(_WIN32)
+        std::wstring wline;
+        if (!std::getline(std::wcin, wline)) {
+            // Input stream is bad or EOF received
+            line.clear();
+            GenerateConsoleCtrlEvent(CTRL_C_EVENT, 0);
+            return false;
+        }
+
+        int size_needed = WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), NULL, 0, NULL, NULL);
+        line.resize(size_needed);
+        WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), &line[0], size_needed, NULL, NULL);
+#else
+        if (!std::getline(std::cin, line)) {
+            // Input stream is bad or EOF received
+            line.clear();
+            return false;
+        }
+#endif
+        if (!line.empty()) {
+            char last = line.back();
+            if (last == '/') { // Always return control on '/' symbol
+                line.pop_back();
+                return false;
+            }
+            if (last == '\\') { // '\\' changes the default action
+                line.pop_back();
+                multiline_input = !multiline_input;
+            }
+        }
+        line += '\n';
+
+        // By default, continue input if multiline_input is set
+        return multiline_input;
+    }
+
+    bool readline(std::string & line, bool multiline_input) {
+        set_display(user_input);
+
+        if (simple_io) {
+            return readline_simple(line, multiline_input);
+        }
+        return readline_advanced(line, multiline_input);
+    }
+
+}
diff --git a/examples/console.h b/examples/console.h
new file mode 100644
index 0000000000000000000000000000000000000000..ec175269b9d8af48803d0b6e618d008a9ab99b4d
--- /dev/null
+++ b/examples/console.h
@@ -0,0 +1,19 @@
+// Console functions
+
+#pragma once
+
+#include <string>
+
+namespace console {
+    enum display_t {
+        reset = 0,
+        prompt,
+        user_input,
+        error
+    };
+
+    void init(bool use_simple_io, bool use_advanced_display);
+    void cleanup();
+    void set_display(display_t display);
+    bool readline(std::string & line, bool multiline_input);
+}
diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp
index 26563821a10780f9e7ef3d4e7ff9aaa726204ba9..2185b9b0e2839ff652fc8d26f7057a1f897b7257 100644
--- a/examples/embd-input/embd-input-lib.cpp
+++ b/examples/embd-input/embd-input-lib.cpp
@@ -30,7 +30,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) {
     fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
 
     if (params.seed == LLAMA_DEFAULT_SEED) {
-        params.seed = time(NULL);
+        params.seed = uint32_t(time(NULL));
     }
     fprintf(stderr, "%s: seed  = %d\n", __func__, params.seed);
 
diff --git a/examples/grammar-parser.cpp b/examples/grammar-parser.cpp
index 019d5e1bf9cebe47bd08d451919f4ee378080ff1..e76bd11c315983ffa4325a378be0a691ffebe7c6 100644
--- a/examples/grammar-parser.cpp
+++ b/examples/grammar-parser.cpp
@@ -405,7 +405,7 @@ namespace grammar_parser {
             for (size_t i = 0, end = state.rules.size(); i < end; i++) {
                 // fprintf(file, "%zu: ", i);
                 // print_rule_binary(file, state.rules[i]);
-                print_rule(file, i, state.rules[i], symbol_id_names);
+                print_rule(file, uint32_t(i), state.rules[i], symbol_id_names);
                 // fprintf(file, "\n");
             }
         } catch (const std::exception & err) {
diff --git a/examples/json-schema-to-grammar.py b/examples/json-schema-to-grammar.py
new file mode 100644
index 0000000000000000000000000000000000000000..2dccc118a70e82d57d40e0ae27606699dc385884
--- /dev/null
+++ b/examples/json-schema-to-grammar.py
@@ -0,0 +1,132 @@
+import argparse
+import json
+import re
+import sys
+
+# whitespace is constrained to a single space char to prevent model "running away" in
+# whitespace. Also maybe improves generation quality?
+SPACE_RULE = '" "?'
+
+PRIMITIVE_RULES = {
+    'boolean': '("true" | "false") space',
+    'number': '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
+    'integer': '("-"? ([0-9] | [1-9] [0-9]*)) space',
+    'string': r''' "\"" (
+        [^"\\] |
+        "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+      )* "\"" space ''',
+    'null': '"null" space',
+}
+
+INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+')
+GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]')
+GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}
+
+
+class SchemaConverter:
+    def __init__(self, prop_order):
+        self._prop_order = prop_order
+        self._rules = {'space': SPACE_RULE}
+
+    def _format_literal(self, literal):
+        escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub(
+            lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), json.dumps(literal)
+        )
+        return f'"{escaped}"'
+
+    def _add_rule(self, name, rule):
+        esc_name = INVALID_RULE_CHARS_RE.sub('-', name)
+        if esc_name not in self._rules or self._rules[esc_name] == rule:
+            key = esc_name
+        else:
+            i = 0
+            while f'{esc_name}{i}' in self._rules:
+                i += 1
+            key = f'{esc_name}{i}'
+        self._rules[key] = rule
+        return key
+
+    def visit(self, schema, name):
+        schema_type = schema.get('type')
+        rule_name = name or 'root'
+
+        if 'oneOf' in schema or 'anyOf' in schema:
+            rule = ' | '.join((
+                self.visit(alt_schema, f'{name}{"-" if name else ""}{i}')
+                for i, alt_schema in enumerate(schema.get('oneOf') or schema['anyOf'])
+            ))
+            return self._add_rule(rule_name, rule)
+
+        elif 'const' in schema:
+            return self._add_rule(rule_name, self._format_literal(schema['const']))
+
+        elif 'enum' in schema:
+            rule = ' | '.join((self._format_literal(v) for v in schema['enum']))
+            return self._add_rule(rule_name, rule)
+
+        elif schema_type == 'object' and 'properties' in schema:
+            # TODO: `required` keyword
+            prop_order = self._prop_order
+            prop_pairs = sorted(
+                schema['properties'].items(),
+                # sort by position in prop_order (if specified) then by key
+                key=lambda kv: (prop_order.get(kv[0], len(prop_order)), kv[0]),
+            )
+
+            rule = '"{" space'
+            for i, (prop_name, prop_schema) in enumerate(prop_pairs):
+                prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}')
+                if i > 0:
+                    rule += ' "," space'
+                rule += fr' {self._format_literal(prop_name)} space ":" space {prop_rule_name}'
+            rule += ' "}" space'
+
+            return self._add_rule(rule_name, rule)
+
+        elif schema_type == 'array' and 'items' in schema:
+            # TODO `prefixItems` keyword
+            item_rule_name = self.visit(schema['items'], f'{name}{"-" if name else ""}item')
+            rule = f'"[" space ({item_rule_name} ("," space {item_rule_name})*)? "]" space'
+            return self._add_rule(rule_name, rule)
+
+        else:
+            assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}'
+            return self._add_rule(
+                'root' if rule_name == 'root' else schema_type,
+                PRIMITIVE_RULES[schema_type]
+            )
+
+    def format_grammar(self):
+        return '\n'.join((f'{name} ::= {rule}' for name, rule in self._rules.items()))
+
+
+def main(args_in = None):
+    parser = argparse.ArgumentParser(
+        description='''
+            Generates a grammar (suitable for use in ./main) that produces JSON conforming to a
+            given JSON schema. Only a subset of JSON schema features are supported; more may be
+            added in the future.
+        ''',
+    )
+    parser.add_argument(
+        '--prop-order',
+        default=[],
+        type=lambda s: s.split(','),
+        help='''
+            comma-separated property names defining the order of precedence for object properties;
+            properties not specified here are given lower precedence than those that are, and are
+            sorted alphabetically
+        '''
+    )
+    parser.add_argument('schema', help='file containing JSON schema ("-" for stdin)')
+    args = parser.parse_args(args_in)
+
+    schema = json.load(sys.stdin if args.schema == '-' else open(args.schema))
+    prop_order = {name: idx for idx, name in enumerate(args.prop_order)}
+    converter = SchemaConverter(prop_order)
+    converter.visit(schema, '')
+    print(converter.format_grammar())
+
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/llama.vim b/examples/llama.vim
new file mode 100644
index 0000000000000000000000000000000000000000..f03fadfb7a017319eb6aee4bb3fef4acaec2f303
--- /dev/null
+++ b/examples/llama.vim
@@ -0,0 +1,132 @@
+" Requires an already running llama.cpp server
+" To install either copy or symlink to ~/.vim/autoload/llama.vim
+" Then start with either :call llama#doLlamaGen(),
+" or add a keybind to your vimrc such as
+" nnoremap Z :call llama#doLlamaGen()<CR>
+" Similarly, you could add an insert mode keybind with
+" inoremap <C-B> <Cmd>call llama#doLlamaGen()<CR>
+"
+" g:llama_api_url and g:llama_overrides can be configured in your .vimrc
+" let g:llama_api_url = "192.168.1.10:8080"
+" llama_overrides can also be set through buffer/window scopes. For instance
+" autocmd filetype python let b:llama_overrides = {"temp": 0.2}
+" Could be added to your .vimrc to automatically set a lower temperature when
+" editing a python script
+" Additionally, an override dict can be stored at the top of a file
+" !*{"stop": ["User:"]}
+" Could be added to the start of your chatlog.txt to set the stopping token
+" These parameter dicts are merged together from lowest to highest priority:
+" server default -> g:llama_overrides -> w:llama_overrides ->
+" b:llama_overrides -> in file (!*) overrides
+"
+" Sublists (like logit_bias and stop) are overridden, not merged
+" Example override:
+" !*{"logit_bias": [[13, -5], [2, false]], "temperature": 1, "top_k": 5, "top_p": 0.5, "n_predict": 256, "repeat_last_n": 256, "repeat_penalty": 1.17647}
+if !exists("g:llama_api_url")
+    let g:llama_api_url= "127.0.0.1:8080"
+endif
+if !exists("g:llama_overrides")
+   let g:llama_overrides = {}
+endif
+const s:querydata = {"n_predict": 256, "stop": [ "\n" ], "stream": v:true }
+const s:curlcommand = ['curl','--data-raw', "{\"prompt\":\"### System:\"}", '--silent', '--no-buffer', '--request', 'POST', '--url', g:llama_api_url .. '/completion', '--header', "Content-Type: application/json"]
+let s:linedict = {}
+
+func s:callbackHandler(bufn, channel, msg)
+   if len(a:msg) < 3
+      return
+   elseif a:msg[0] == "d"
+      let l:msg = a:msg[6:-1]
+   else
+      let l:msg = a:msg
+   endif
+   let l:decoded_msg = json_decode(l:msg)
+   let l:newtext = split(l:decoded_msg['content'], "\n", 1)
+   if len(l:newtext) > 0
+      call setbufline(a:bufn, s:linedict[a:bufn], getbufline(a:bufn, s:linedict[a:bufn])[0] .. newtext[0])
+   else
+      echo "nothing genned"
+   endif
+   if len(newtext) > 1
+      let l:failed = appendbufline(a:bufn, s:linedict[a:bufn], newtext[1:-1])
+      let s:linedict[a:bufn] = s:linedict[a:bufn] + len(newtext)-1
+   endif
+   if has_key(l:decoded_msg, "stop") && l:decoded_msg.stop
+       echo "Finished generation"
+   endif
+endfunction
+
+func llama#doLlamaGen()
+   if exists("b:job")
+      if job_status(b:job) == "run"
+         call job_stop(b:job)
+         return
+      endif
+   endif
+
+   let l:cbuffer = bufnr("%")
+   let s:linedict[l:cbuffer] = line('$')
+   let l:buflines = getbufline(l:cbuffer, 1, 1000)
+   let l:querydata = copy(s:querydata)
+   call extend(l:querydata, g:llama_overrides)
+   if exists("w:llama_overrides")
+      call extend(l:querydata, w:llama_overrides)
+   endif
+   if exists("b:llama_overrides")
+      call extend(l:querydata, b:llama_overrides)
+   endif
+   if l:buflines[0][0:1] == '!*'
+      let l:userdata = json_decode(l:buflines[0][2:-1])
+      call extend(l:querydata, l:userdata)
+      let l:buflines = l:buflines[1:-1]
+   endif
+   let l:querydata.prompt = join(l:buflines, "\n")
+   let l:curlcommand = copy(s:curlcommand)
+   let l:curlcommand[2] = json_encode(l:querydata)
+   let b:job = job_start(l:curlcommand, {"callback": function("s:callbackHandler", [l:cbuffer])})
+endfunction
+
+" Echos the tokkenization of the provided string , or cursor to end of word
+" Onus is placed on the user to include the preceding space
+func llama#tokenizeWord(...)
+    if (a:0 > 0)
+        let l:input = a:1
+    else
+        exe "normal \"*ye"
+        let l:input = @*
+    endif
+    let l:querydata = {"content": l:input}
+    let l:curlcommand = copy(s:curlcommand)
+    let l:curlcommand[2] = json_encode(l:querydata)
+    let l:curlcommand[8] = g:llama_api_url .. "/tokenize"
+   let s:token_job = job_start(l:curlcommand, {"callback": function("s:tokenizeWordCallback", [l:input])})
+endfunction
+
+func s:tokenizeWordCallback(plaintext, channel, msg)
+    echo '"' .. a:plaintext ..'" - ' .. string(json_decode(a:msg).tokens)
+endfunction
+
+
+" Echos the token count of the entire buffer (or provided string)
+" Example usage :echo llama#tokenCount()
+func llama#tokenCount(...)
+    if (a:0 > 0)
+        let l:buflines = a:1
+    else
+        let l:buflines = getline(1,1000)
+        if l:buflines[0][0:1] == '!*'
+            let l:buflines = l:buflines[1:-1]
+        endif
+        let l:buflines = join(l:buflines, "\n")
+    endif
+    let l:querydata = {"content": l:buflines}
+    let l:curlcommand = copy(s:curlcommand)
+    let l:curlcommand[2] = json_encode(l:querydata)
+    let l:curlcommand[8] = g:llama_api_url .. "/tokenize"
+   let s:token_job = job_start(l:curlcommand, {"callback": "s:tokenCountCallback"})
+endfunction
+
+func s:tokenCountCallback(channel, msg)
+    let resp = json_decode(a:msg)
+    echo len(resp.tokens)
+endfunction
diff --git a/examples/llm.vim b/examples/llm.vim
index efecad0cd89f16015347b014e376725b0978068e..594a285493dccb3f127f1e55fed230173d6f9a03 100644
--- a/examples/llm.vim
+++ b/examples/llm.vim
@@ -1,3 +1,5 @@
+" Basic plugin example
+
 function! Llm()
 
   let url = "http://127.0.0.1:8080/completion"
@@ -16,8 +18,10 @@ function! Llm()
   " Extract the content field from the response
   let content = json_decode(response).content
 
+  let split_newlines = split(content, '\n', 1)
+
   " Insert the content at the cursor position
-  call setline(line('.'), getline('.') . content)
+  call setline(line('.'), [ getline('.') . split_newlines[0] ] + split_newlines[1:])
 endfunction
 
 command! Llm call Llm()
diff --git a/examples/main/README.md b/examples/main/README.md
index 37538613042b0f23980115b3358c173f4cd38fa7..55c16096f03b793813816607c9f07dd275b2b953 100644
--- a/examples/main/README.md
+++ b/examples/main/README.md
@@ -140,6 +140,12 @@ The `--ctx-size` option allows you to set the size of the prompt context used by
 
 -   `-c N, --ctx-size N`: Set the size of the prompt context (default: 512). The LLaMA models were built with a context of 2048, which will yield the best results on longer input/inference. However, increasing the context size beyond 2048 may lead to unpredictable results.
 
+### Extended Context Size
+
+Some fine-tuned models have extened the context length by scaling RoPE. For example, if the original pretrained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.
+
+- `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model.
+
 ### Keep Prompt
 
 The `--keep` option allows users to retain the original prompt when the model runs out of context, ensuring a connection to the initial instruction or conversation topic is maintained.
@@ -202,9 +208,9 @@ Example usage: `--top-p 0.95`
 
 -   `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
 
-Tail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. The method adjusts the logits (token probabilities) by raising them to the power of the parameter z. A higher value of z (e.g., 2.0) will further suppress less likely tokens from the tail of the distribution, while a value of 1.0 disables the effect of TFS. By setting the parameter z, you can control how much the probabilities of less likely tokens are reduced.
+Tail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens, and thus disables the effect of TFS.
 
-Example usage: `--tfs 2.0`
+Example usage: `--tfs 0.95`
 
 ### Locally Typical Sampling
 
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 3796a9230113653402911ffbde6e0d21d44f4ce9..56ada7e69d99dd2c5656dfd3752078547875906f 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -4,6 +4,7 @@
 #endif
 
 #include "common.h"
+#include "console.h"
 #include "llama.h"
 #include "build-info.h"
 #include "grammar-parser.h"
@@ -35,9 +36,7 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-static console_state con_st;
 static llama_context ** g_ctx;
-
 static bool is_interacting = false;
 
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
@@ -46,7 +45,7 @@ void sigint_handler(int signo) {
         if (!is_interacting) {
             is_interacting=true;
         } else {
-            console_cleanup(con_st);
+            console::cleanup();
             printf("\n");
             llama_print_timings(*g_ctx);
             _exit(130);
@@ -64,10 +63,8 @@ int main(int argc, char ** argv) {
 
     // save choice to use color for later
     // (note for later: this is a slightly awkward choice)
-    con_st.use_color = params.use_color;
-    con_st.multiline_input = params.multiline_input;
-    console_init(con_st);
-    atexit([]() { console_cleanup(con_st); });
+    console::init(params.simple_io, params.use_color);
+    atexit([]() { console::cleanup(); });
 
     if (params.perplexity) {
         printf("\n************\n");
@@ -373,7 +370,7 @@ int main(int argc, char ** argv) {
 
     if (params.interactive) {
         const char *control_message;
-        if (con_st.multiline_input) {
+        if (params.multiline_input) {
             control_message = " - To return control to LLaMa, end your input with '\\'.\n"
                               " - To return control without starting a new line, end your input with '/'.\n";
         } else {
@@ -401,7 +398,7 @@ int main(int argc, char ** argv) {
     int n_past_guidance    = 0;
 
     // the first thing we will do is to output the prompt, so set color accordingly
-    console_set_color(con_st, CONSOLE_COLOR_PROMPT);
+    console::set_display(console::prompt);
 
     std::vector<llama_token> embd;
     std::vector<llama_token> embd_guidance;
@@ -422,9 +419,9 @@ int main(int argc, char ** argv) {
             // Ensure the input doesn't exceed the context size by truncating embd if necessary.
             if ((int)embd.size() > max_embd_size) {
                 auto skipped_tokens = embd.size() - max_embd_size;
-                console_set_color(con_st, CONSOLE_COLOR_ERROR);
+                console::set_display(console::error);
                 printf("<<input too long: skipped %zu token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
-                console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
+                console::set_display(console::reset);
                 fflush(stdout);
                 embd.resize(max_embd_size);
             }
@@ -667,7 +664,7 @@ int main(int argc, char ** argv) {
         }
         // reset color to default if we there is no pending user input
         if (input_echo && (int)embd_inp.size() == n_consumed) {
-            console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
+            console::set_display(console::reset);
         }
 
         // if not currently processing queued inputs;
@@ -693,7 +690,7 @@ int main(int argc, char ** argv) {
                     if (last_output.find(antiprompt.c_str(), search_start_pos) != std::string::npos) {
                         if (params.interactive) {
                             is_interacting = true;
-                            console_set_color(con_st, CONSOLE_COLOR_USER_INPUT);
+                            console::set_display(console::user_input);
                         }
                         is_antiprompt = true;
                         fflush(stdout);
@@ -714,7 +711,7 @@ int main(int argc, char ** argv) {
 
                     is_interacting = true;
                     printf("\n");
-                    console_set_color(con_st, CONSOLE_COLOR_USER_INPUT);
+                    console::set_display(console::user_input);
                     fflush(stdout);
                 } else if (params.instruct) {
                     is_interacting = true;
@@ -739,12 +736,12 @@ int main(int argc, char ** argv) {
                 std::string line;
                 bool another_line = true;
                 do {
-                    another_line = console_readline(con_st, line);
+                    another_line = console::readline(line, params.multiline_input);
                     buffer += line;
                 } while (another_line);
 
                 // done taking input, reset color
-                console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
+                console::set_display(console::reset);
 
                 // Add tokens to embd only if the input buffer is non-empty
                 // Entering a empty line lets the user pass control back
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index d23b7e7f0c1b8f2ea693178c5a16466d5df081f7..62433e983df9c4cb770ff36516685a9a3d55610a 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -121,8 +121,23 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
     printf("\n");
 }
 
-void perplexity_lines(llama_context * ctx, const gpt_params & params) {
-    // Calculates perplexity over each line of the prompt
+void hellaswag_score(llama_context * ctx, const gpt_params & params) {
+    // Calculates hellaswag score (acc_norm) from prompt
+    //
+    // Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
+    // All used data fields are preprocessed as in https://github.com/EleutherAI/lm-evaluation-harness/blob/df3da98c5405deafd519c2ddca52bb7c3fe36bef/lm_eval/tasks/hellaswag.py#L62-L68
+    //
+    // All 10042 tasks should be extracted to keep the results standardized like other implementations.
+    //
+    // Datafile layout:
+    // ['??'] denotes json fields
+    // 6 lines per task:
+    // ['activity_label'] + ": " +['ctx']  - The first part of the query, the context
+    // ['label'] - The index the best common sense ending aka gold ending
+    // ['endings'][0] - Endings added to the first part of the query
+    // ['endings'][1]
+    // ['endings'][2]
+    // ['endings'][3]
 
     std::vector<std::string> prompt_lines;
     std::istringstream strstream(params.prompt);
@@ -132,63 +147,149 @@ void perplexity_lines(llama_context * ctx, const gpt_params & params) {
         prompt_lines.push_back(line);
     }
 
-    const int n_vocab = llama_n_vocab(ctx);
+    if( prompt_lines.size() % 6 != 0) {
+        fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__);
+        return;
+    }
 
-    int counttotal   = 0;
-    size_t n_lines = prompt_lines.size();
+    size_t hs_task_count = prompt_lines.size()/6;
+    fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
 
-    double nll = 0.0;
+    // This is needed as usual for LLaMA models
+    bool prepend_bos = true;
+
+    // Number of tasks to use when computing the score
+    if ( params.hellaswag_tasks < hs_task_count  ) {
+        hs_task_count = params.hellaswag_tasks;
+    }
 
-    fprintf(stderr, "%s: calculating perplexity over %lu lines\n", __func__, n_lines);
+    // The tasks should be randomized so the score stabilizes quickly.
+    bool randomize_tasks = true;
 
-    printf("\nLine\tPPL line\tPPL cumulative\n");
+    // The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now
+    std::mt19937 rng(1);
 
-    for (size_t i = 0; i < n_lines; ++i) {
+    // Dataholder for hellaswag tasks
+    struct hs_data_t {
+        std::string context;
+        size_t gold_ending_idx;
+        std::string ending[4];
+        size_t ending_logprob_count[4];
+        double ending_logprob[4];
+    };
 
-        // Tokenize and insert BOS at start
-        std::vector<int> batch_embd = ::llama_tokenize(ctx, prompt_lines[i], true);
+    fprintf(stderr, "%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first")  );
 
-        size_t batch_size  = batch_embd.size();
+    // Select and read data from prompt lines
+    hs_data_t *hs_data = new hs_data_t[hs_task_count];
+    for (size_t i=0; i < hs_task_count; i++) {
+        size_t idx = i;
 
-        // Stop if line is too long
-        if( batch_size > (size_t)params.n_ctx ) {
-            fprintf(stderr, "%s : tokens in line %lu > n_ctxl\n", __func__, i);
-            return;
+        // Select a random example of those left in the prompt
+        if (randomize_tasks) {
+            std::uniform_int_distribution<size_t> dist(0, prompt_lines.size()/6-1 ) ;
+            idx = dist(rng);
         }
 
-        if (llama_eval(ctx, batch_embd.data(), batch_size, 0, params.n_threads)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
-            return;
+        hs_data[i].context = prompt_lines[idx*6];
+        hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
+        for (size_t j=0; j < 4; j++) {
+            hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j];
         }
 
-        const auto batch_logits = llama_get_logits(ctx);
-        std::vector<float> logits;
-        logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
+        // Delete the selected random example from the prompt
+        if (randomize_tasks) {
+            prompt_lines.erase( std::next(prompt_lines.begin(),idx*6)  , std::next(prompt_lines.begin(),idx*6+6) );
+        }
+    }
 
-        double nllline = 0.0;
-        int countline = 0;
+    fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__);
+    printf("\ntask\tacc_norm\n");
 
-        // Perplexity over second half of the line
-        for (size_t j = batch_size/2; j < batch_size - 1; ++j) {
-            // Calculate probability of next token, given the previous ones.
-            const std::vector<float> tok_logits(
-                logits.begin() + (j + 0) * n_vocab,
-                logits.begin() + (j + 1) * n_vocab);
+    double acc = 0.0f;
+    const int n_vocab = llama_n_vocab(ctx);
+
+    for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
+
+        // Tokenize the context to count tokens
+        std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos);
+        size_t context_size = context_embd.size();
+
+        for (size_t ending_idx=0;ending_idx<4;ending_idx++) {
+
+            // Tokenize the query
+            std::vector<int> query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[ending_idx], prepend_bos);
+            size_t query_size = query_embd.size();
+
+            // Stop if query wont fit the ctx window
+            if (query_size > (size_t)params.n_ctx) {
+                fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
+                return;
+            }
 
-            const float prob = softmax(tok_logits)[batch_embd[ j + 1]];
+            // Speedup small evaluations by evaluating atleast 32 tokens
+            if (query_size < 32) {
+                query_embd.resize(32);
+            }
+
+            // Evaluate the query
+            if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads)) {
+                fprintf(stderr, "%s : failed to eval\n", __func__);
+                return;
+            }
+
+            const auto query_logits = llama_get_logits(ctx);
+            std::vector<float> logits;
+            logits.insert(logits.end(), query_logits, query_logits + query_size * n_vocab);
+
+            hs_data[task_idx].ending_logprob_count[ending_idx] = 0;
+            hs_data[task_idx].ending_logprob[ending_idx] = 0.0f;
+
+            // Calculate the logprobs over the ending
+            for (size_t j = context_size-1; j < query_size - 1; j++) {
+                // Calculate probability of next token, given the previous ones.
+                const std::vector<float> tok_logits(
+                    logits.begin() + (j + 0) * n_vocab,
+                    logits.begin() + (j + 1) * n_vocab);
+
+                const float prob = softmax(tok_logits)[query_embd[ j + 1]];
+
+                hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob);
+                hs_data[task_idx].ending_logprob_count[ending_idx]++;
+            }
+
+            // Calculate the mean token logprob for acc_norm
+            hs_data[task_idx].ending_logprob[ending_idx] /= hs_data[task_idx].ending_logprob_count[ending_idx];
+
+
+//            printf("task %lu, ending %lu, whole_len %lu, context_len %lu, ending_logprob_count %lu, ending_logprob %.4f\n",
+//                task_idx,ending_idx,whole_size,context_size, hs_data[task_idx].ending_logprob_count[ending_idx], hs_data[task_idx].ending_logprob[ending_idx] );
+        }
 
-            nllline += -std::log(prob);
-            ++countline;
+        // Find the ending with maximum logprob
+        size_t ending_logprob_max_idx = -1;
+        double ending_logprob_max_val = -INFINITY;
+        for (size_t j=0; j < 4; j++) {
+            if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) {
+                ending_logprob_max_idx = j;
+                ending_logprob_max_val =  hs_data[task_idx].ending_logprob[j];
+            }
         }
 
-        nll += nllline;
-        counttotal += countline;
+//        printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx);
 
-        // perplexity is e^(average negative log-likelihood)
-        printf("%lu\t%.8lf\t%.8lf\n", i + 1, std::exp(nllline/countline), std::exp(nll / counttotal) );
+        // If the gold ending got the maximum logprobe add one accuracy point
+        if (ending_logprob_max_idx == hs_data[task_idx].gold_ending_idx) {
+            acc += 1.0;
+        }
+
+        // Print the accumulated accuracy mean x 100
+        printf("%zu\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0);
         fflush(stdout);
     }
 
+    delete [] hs_data;
+
     printf("\n");
 }
 
@@ -240,8 +341,8 @@ int main(int argc, char ** argv) {
                 params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
     }
 
-    if (params.perplexity_lines) {
-        perplexity_lines(ctx, params);
+    if (params.hellaswag) {
+        hellaswag_score(ctx, params);
     } else {
         perplexity(ctx, params);
     }
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index 4c868850317fe413a84f5c7216f56d7e952787a0..61c71c3589fdf28869adbad4fa22e05c56fb110e 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -26,6 +26,7 @@ int main(int argc, char ** argv) {
     auto lparams = llama_context_default_params();
 
     lparams.n_ctx     = params.n_ctx;
+    lparams.n_gqa     = params.n_gqa;
     lparams.seed      = params.seed;
     lparams.f16_kv    = params.memory_f16;
     lparams.use_mmap  = params.use_mmap;
diff --git a/examples/server-llama2-13B.sh b/examples/server-llama2-13B.sh
new file mode 100644
index 0000000000000000000000000000000000000000..17fedc2b176f611fe508e4d38a5a035a4c21bb55
--- /dev/null
+++ b/examples/server-llama2-13B.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+set -e
+
+cd "$(dirname "$0")/.." || exit
+
+# Specify the model you want to use here:
+MODEL="${MODEL:-./models/llama-2-13b-chat.ggmlv3.q5_K_M.bin}"
+PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat-system.txt}
+
+# Adjust to the number of CPU cores you want to use.
+N_THREAD="${N_THREAD:-12}"
+
+# Note: you can also override the generation options by specifying them on the command line:
+GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 4096 --batch-size 1024}"
+
+
+# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
+./server $GEN_OPTIONS \
+  --model "$MODEL" \
+  --threads "$N_THREAD" \
+  --rope-freq-scale 1.0 \
+  "$@"
+
+# I used this to test the model with mps, but omitted it from the general purpose. If you want to use it, just specify it on the command line.
+# -ngl 1 \
diff --git a/examples/server/README.md b/examples/server/README.md
index e5ca8269b9d5646e93744ed250d2404196e6e8ca..e56ca063a9f0e66e3d44e2fd91bfa63b66e525ae 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -151,6 +151,8 @@ node .
 
     `mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
 
+    `grammar`: Set grammar for grammar-based sampling (default: no grammar)
+
     `seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
 
     `ignore_eos`: Ignore end of stream token and continue generating (default: false).
@@ -163,7 +165,7 @@ node .
 
     `content`: Set the text to tokenize.
 
-    Note that the special `BOS` token is not added in fron of the text and also a space character is not inserted automatically as it is for `/completion`.
+    Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
 
 -   **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.
 
diff --git a/examples/server/chat-llama2.sh b/examples/server/chat-llama2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1fc79b7e191374b434b645951610a68e55537305
--- /dev/null
+++ b/examples/server/chat-llama2.sh
@@ -0,0 +1,109 @@
+#!/bin/bash
+
+API_URL="${API_URL:-http://127.0.0.1:8080}"
+
+CHAT=(
+    "Hello, Assistant."
+    "Hello. How may I help you today?"
+)
+
+INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
+
+trim() {
+    shopt -s extglob
+    set -- "${1##+([[:space:]])}"
+    printf "%s" "${1%%+([[:space:]])}"
+}
+
+trim_trailing() {
+    shopt -s extglob
+    printf "%s" "${1%%+([[:space:]])}"
+}
+
+format_prompt() {
+    if [[ "${#CHAT[@]}" -eq 0 ]]; then
+        echo -n "[INST] <<SYS>>\n${INSTRUCTION}\n<</SYS>>"
+    else
+        LAST_INDEX=$(( ${#CHAT[@]} - 1 ))
+        echo -n "${CHAT[$LAST_INDEX]}\n[INST] $1 [/INST]"
+    fi
+}
+
+tokenize() {
+    curl \
+        --silent \
+        --request POST \
+        --url "${API_URL}/tokenize" \
+        --header "Content-Type: application/json" \
+        --data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
+    | jq '.tokens[]'
+}
+
+N_KEEP=$(tokenize "[INST] <<SYS>>\n${INSTRUCTION}\n<</SYS>>" | wc -l)
+
+chat_completion() {
+    PROMPT="$(trim_trailing "$(format_prompt "$1")")"
+    DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{
+        prompt: .,
+        temperature: 0.2,
+        top_k: 40,
+        top_p: 0.9,
+        n_keep: $n_keep,
+        n_predict: 1024,
+        stop: ["[INST]"],
+        stream: true
+    }')"
+
+    # Create a temporary file to hold the Python output
+    TEMPFILE=$(mktemp)
+
+    exec 3< <(curl \
+        --silent \
+        --no-buffer \
+        --request POST \
+        --url "${API_URL}/completion" \
+        --header "Content-Type: application/json" \
+        --data-raw "${DATA}")
+
+    python -c "
+import json
+import sys
+
+answer = ''
+while True:
+    line = sys.stdin.readline()
+    if not line:
+        break
+    if line.startswith('data: '):
+        json_content = line[6:].strip()
+        content = json.loads(json_content)['content']
+        sys.stdout.write(content)
+        sys.stdout.flush()
+        answer += content
+
+answer = answer.rstrip('\n')
+
+# Write the answer to the temporary file
+with open('$TEMPFILE', 'w') as f:
+    f.write(answer)
+    " <&3
+
+    exec 3<&-
+
+    # Read the answer from the temporary file
+    ANSWER=$(cat $TEMPFILE)
+
+    # Clean up the temporary file
+    rm $TEMPFILE
+
+    printf "\n"
+
+    CHAT+=("$1" "$(trim "$ANSWER")")
+}
+
+while true; do
+    echo -en "\033[0;32m"  # Green color
+    read -r -e -p "> " QUESTION
+    echo -en "\033[0m"  # Reset color
+    chat_completion "${QUESTION}"
+done
diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp
index f399fb19a6bc8651b764b0245ff9608c3dfb41e3..f0a071a69bca1cbf8cf997225c21233ed3c9a54c 100644
--- a/examples/server/completion.js.hpp
+++ b/examples/server/completion.js.hpp
@@ -87,289 +87,342 @@ unsigned char completion_js[] = {
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 };
-unsigned int completion_js_len = 4462;
+unsigned int completion_js_len = 5099;
diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
index 1b21d4d55550b54966c4bf0d70e41acd23d33767..bd0b4787068e096e46db5322f431a3e037abf23a 100644
--- a/examples/server/index.html.hpp
+++ b/examples/server/index.html.hpp
@@ -10,642 +10,672 @@ unsigned char index_html[] = {
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+  0x61, 0x64, 0x65, 0x72, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6d, 0x61, 0x69, 0x6e, 0x20, 0x69,
+  0x64, 0x3d, 0x22, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x22, 0x3e,
   0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
-  0x20, 0x3c, 0x70, 0x3e, 0x3c, 0x24, 0x7b, 0x4d, 0x6f, 0x64, 0x65, 0x6c,
-  0x47, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, 0x6e,
-  0x66, 0x6f, 0x7d, 0x20, 0x2f, 0x3e, 0x3c, 0x2f, 0x70, 0x3e, 0x0a, 0x20,
-  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c,
-  0x70, 0x3e, 0x50, 0x6f, 0x77, 0x65, 0x72, 0x65, 0x64, 0x20, 0x62, 0x79,
-  0x20, 0x3c, 0x61, 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d, 0x22, 0x68, 0x74,
-  0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x69, 0x74, 0x68, 0x75, 0x62,
-  0x2e, 0x63, 0x6f, 0x6d, 0x2f, 0x67, 0x67, 0x65, 0x72, 0x67, 0x61, 0x6e,
-  0x6f, 0x76, 0x2f, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70,
-  0x22, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x3c,
-  0x2f, 0x61, 0x3e, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x3c, 0x61, 0x20, 0x68,
-  0x72, 0x65, 0x66, 0x3d, 0x22, 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f,
-  0x2f, 0x67, 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x22, 0x3e, 0x67, 0x67,
-  0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x3c, 0x2f, 0x61, 0x3e, 0x2e, 0x3c, 0x2f,
-  0x70, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
-  0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20,
-  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76,
-  0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x3b, 0x0a, 0x20,
-  0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65,
-  0x6e, 0x64, 0x65, 0x72, 0x28, 0x68, 0x28, 0x41, 0x70, 0x70, 0x29, 0x2c,
-  0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x62, 0x6f,
-  0x64, 0x79, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x63, 0x72,
-  0x69, 0x70, 0x74, 0x3e, 0x0a, 0x3c, 0x2f, 0x68, 0x65, 0x61, 0x64, 0x3e,
-  0x0a, 0x0a, 0x3c, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x3c, 0x2f, 0x62,
-  0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c,
-  0x3e, 0x0a
+  0x20, 0x3c, 0x24, 0x7b, 0x63, 0x68, 0x61, 0x74, 0x53, 0x74, 0x61, 0x72,
+  0x74, 0x65, 0x64, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3f, 0x20,
+  0x43, 0x68, 0x61, 0x74, 0x4c, 0x6f, 0x67, 0x20, 0x3a, 0x20, 0x43, 0x6f,
+  0x6e, 0x66, 0x69, 0x67, 0x46, 0x6f, 0x72, 0x6d, 0x7d, 0x20, 0x2f, 0x3e,
+  0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c,
+  0x2f, 0x6d, 0x61, 0x69, 0x6e, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x65, 0x63, 0x74, 0x69,
+  0x6f, 0x6e, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x77, 0x72, 0x69, 0x74, 0x65,
+  0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67,
+  0x65, 0x49, 0x6e, 0x70, 0x75, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x73,
+  0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x6f, 0x6f, 0x74,
+  0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x3c, 0x70, 0x3e, 0x3c, 0x24, 0x7b, 0x4d, 0x6f,
+  0x64, 0x65, 0x6c, 0x47, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f,
+  0x6e, 0x49, 0x6e, 0x66, 0x6f, 0x7d, 0x20, 0x2f, 0x3e, 0x3c, 0x2f, 0x70,
+  0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x3c, 0x70, 0x3e, 0x50, 0x6f, 0x77, 0x65, 0x72, 0x65, 0x64,
+  0x20, 0x62, 0x79, 0x20, 0x3c, 0x61, 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d,
+  0x22, 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x69, 0x74,
+  0x68, 0x75, 0x62, 0x2e, 0x63, 0x6f, 0x6d, 0x2f, 0x67, 0x67, 0x65, 0x72,
+  0x67, 0x61, 0x6e, 0x6f, 0x76, 0x2f, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e,
+  0x63, 0x70, 0x70, 0x22, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63,
+  0x70, 0x70, 0x3c, 0x2f, 0x61, 0x3e, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x3c,
+  0x61, 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d, 0x22, 0x68, 0x74, 0x74, 0x70,
+  0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x22,
+  0x3e, 0x67, 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x3c, 0x2f, 0x61, 0x3e,
+  0x2e, 0x3c, 0x2f, 0x70, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72,
+  0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f,
+  0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60,
+  0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20,
+  0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x68, 0x28, 0x41, 0x70,
+  0x70, 0x29, 0x2c, 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74,
+  0x2e, 0x62, 0x6f, 0x64, 0x79, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x3c, 0x2f,
+  0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x3e, 0x0a, 0x3c, 0x2f, 0x68, 0x65,
+  0x61, 0x64, 0x3e, 0x0a, 0x0a, 0x3c, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a,
+  0x3c, 0x2f, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68,
+  0x74, 0x6d, 0x6c, 0x3e, 0x0a
 };
-unsigned int index_html_len = 13790;
+unsigned int index_html_len = 13781;
diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js
index a43d5a7d56753b9495d63ada859f6273003b1a1a..0c9bd5f1021dbd2204688798d30e56c04eba073a 100644
--- a/examples/server/public/completion.js
+++ b/examples/server/public/completion.js
@@ -43,6 +43,7 @@ export async function* llama(prompt, params = {}, config = {}) {
   const decoder = new TextDecoder();
 
   let content = "";
+  let leftover = ""; // Buffer for partially read lines
 
   try {
     let cont = true;
@@ -53,29 +54,47 @@ export async function* llama(prompt, params = {}, config = {}) {
         break;
       }
 
-      // sse answers in the form multiple lines of: value\n with data always present as a key. in our case we
-      // mainly care about the data: key here, which we expect as json
-      const text = decoder.decode(result.value);
+      // Add any leftover data to the current chunk of data
+      const text = leftover + decoder.decode(result.value);
 
-      // parse all sse events and add them to result
-      const regex = /^(\S+):\s(.*)$/gm;
-      for (const match of text.matchAll(regex)) {
-        result[match[1]] = match[2]
-      }
+      // Check if the last character is a line break
+      const endsWithLineBreak = text.endsWith('\n');
 
-      // since we know this is llama.cpp, let's just decode the json in data
-      result.data = JSON.parse(result.data);
-      content += result.data.content;
+      // Split the text into lines
+      let lines = text.split('\n');
 
-      // yield
-      yield result;
+      // If the text doesn't end with a line break, then the last line is incomplete
+      // Store it in leftover to be added to the next chunk of data
+      if (!endsWithLineBreak) {
+        leftover = lines.pop();
+      } else {
+        leftover = ""; // Reset leftover if we have a line break at the end
+      }
 
-      // if we got a stop token from server, we will break here
-      if (result.data.stop) {
-        if (result.data.generation_settings) {
-          generation_settings = result.data.generation_settings;
+      // Parse all sse events and add them to result
+      const regex = /^(\S+):\s(.*)$/gm;
+      for (const line of lines) {
+        const match = regex.exec(line);
+        if (match) {
+          result[match[1]] = match[2]
+          // since we know this is llama.cpp, let's just decode the json in data
+          if (result.data) {
+            result.data = JSON.parse(result.data);
+            content += result.data.content;
+
+            // yield
+            yield result;
+
+            // if we got a stop token from server, we will break here
+            if (result.data.stop) {
+              if (result.data.generation_settings) {
+                generation_settings = result.data.generation_settings;
+              }
+              cont = false;
+              break;
+            }
+          }
         }
-        break;
       }
     }
   } catch (e) {
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index ea93de4aafc486a7c619f8fe99307c502eb48107..de41da187a40f4413738e3cf7dc93f591b71546d 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -3,12 +3,11 @@
 <head>
   <meta charset="UTF-8">
   <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
+  <meta name="color-scheme" content="light dark">
   <title>llama.cpp - chat</title>
 
   <style>
     body {
-      background-color: #fff;
-      color: #000;
       font-family: system-ui;
       font-size: 90%;
     }
@@ -283,8 +282,9 @@
 
       useEffect(() => {
         // scroll to bottom (if needed)
-        if (container.current && container.current.scrollHeight <= container.current.scrollTop + container.current.offsetHeight + 300) {
-          container.current.scrollTo(0, container.current.scrollHeight)
+        const parent = container.current.parentElement;
+        if (parent && parent.scrollHeight <= parent.scrollTop + parent.offsetHeight + 300) {
+          parent.scrollTo(0, parent.scrollHeight)
         }
       }, [messages])
 
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 83c03065a5d583daf150026f796532c165a8e224..10ae264f516f4e80af02b7c320fa45a24cc06669 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1,6 +1,7 @@
 #include "common.h"
 #include "llama.h"
 #include "build-info.h"
+#include "grammar-parser.h"
 
 #ifndef NDEBUG
 // crash the server in debug mode, otherwise send an http 500 error
@@ -195,6 +196,8 @@ struct llama_server_context
     llama_context *ctx = nullptr;
     gpt_params params;
 
+    llama_grammar *grammar = nullptr;
+
     bool truncated = false;
     bool stopped_eos = false;
     bool stopped_word = false;
@@ -226,6 +229,7 @@ struct llama_server_context
     void rewind()
     {
         params.antiprompt.clear();
+        params.grammar.clear();
         num_prompt_tokens = 0;
         num_tokens_predicted = 0;
         generated_text = "";
@@ -237,6 +241,7 @@ struct llama_server_context
         stopped_limit = false;
         stopping_word = "";
         multibyte_pending = 0;
+        grammar = nullptr;
 
         n_remain = 0;
         n_past = 0;
@@ -257,6 +262,33 @@ struct llama_server_context
         return true;
     }
 
+    bool loadGrammar()
+    {
+        if (!params.grammar.empty()) {
+            grammar_parser::parse_state parsed_grammar;
+
+            parsed_grammar = grammar_parser::parse(params.grammar.c_str());
+            // will be empty (default) if there are parse errors
+            if (parsed_grammar.rules.empty()) {
+                LOG_ERROR("grammar parse error", {{"grammar", params.grammar}});
+                return false;
+            }
+            grammar_parser::print_grammar(stderr, parsed_grammar);
+
+            {
+                auto it = params.logit_bias.find(llama_token_eos());
+                if (it != params.logit_bias.end() && it->second == -INFINITY) {
+                    LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {});
+                }
+            }
+
+            std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
+            grammar = llama_grammar_init(
+                grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
+        }
+        return true;
+    }
+
     void loadPrompt()
     {
         params.prompt.insert(0, 1, ' '); // always add a first space
@@ -420,6 +452,10 @@ struct llama_server_context
                 logits[llama_token_nl()] = nl_logit;
             }
 
+            if (grammar != nullptr) {
+                llama_sample_grammar(ctx, &candidates_p, grammar);
+            }
+
             if (temp <= 0)
             {
                 // Greedy sampling
@@ -457,10 +493,15 @@ struct llama_server_context
                 }
             }
 
+            if (grammar != nullptr) {
+                llama_grammar_accept_token(ctx, grammar, result.tok);
+            }
+
             for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i)
             {
                 result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p});
             }
+
             last_n_tokens.erase(last_n_tokens.begin());
             last_n_tokens.push_back(result.tok);
             num_tokens_predicted++;
@@ -631,6 +672,9 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     fprintf(stdout, "                        how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
     fprintf(stdout, "  -mg i, --main-gpu i   the GPU to use for scratch and small tensors\n");
     fprintf(stdout, "  -lv, --low-vram don't allocate VRAM scratch buffer\n");
+    fprintf(stdout, "  -mmq, --mul-mat-q     use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
+    fprintf(stdout, "                        Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
+    fprintf(stdout, "                        is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
 #endif
     fprintf(stdout, "  -m FNAME, --model FNAME\n");
     fprintf(stdout, "                        model path (default: %s)\n", params.model.c_str());
@@ -827,7 +871,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 }
             }
 #else
-            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.", {});
+            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
 #endif // GGML_USE_CUBLAS
         }
         else if (arg == "--low-vram" || arg == "-lv")
@@ -835,7 +879,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
 #ifdef GGML_USE_CUBLAS
             params.low_vram = true;
 #else
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
+            LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n", {});
+#endif // GGML_USE_CUBLAS
+        }
+        else if (arg == "--mul-mat-q" || arg == "-mmq")
+        {
+#ifdef GGML_USE_CUBLAS
+            params.mul_mat_q = true;
+#else
+            LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n", {});
 #endif // GGML_USE_CUBLAS
         }
         else if (arg == "--main-gpu" || arg == "-mg")
@@ -936,6 +988,7 @@ static json format_generation_settings(llama_server_context &llama)
         {"stream", llama.stream},
         {"logit_bias", llama.params.logit_bias},
         {"n_probs", llama.params.n_probs},
+        {"grammar", llama.params.grammar},
     };
 }
 
@@ -1037,6 +1090,7 @@ static void parse_options_completion(const json &body, llama_server_context &lla
     llama.params.n_keep = body.value("n_keep", default_params.n_keep);
     llama.params.seed = body.value("seed", default_params.seed);
     llama.params.prompt = body.value("prompt", default_params.prompt);
+    llama.params.grammar = body.value("grammar", default_params.grammar);
     llama.params.n_probs = body.value("n_probs", default_params.n_probs);
 
     llama.params.logit_bias.clear();
@@ -1168,6 +1222,12 @@ int main(int argc, char **argv)
 
         parse_options_completion(json::parse(req.body), llama);
 
+        if (!llama.loadGrammar())
+        {
+            res.status = 400;
+            return;
+        }
+
         llama.loadPrompt();
         llama.beginCompletion();
 
@@ -1263,7 +1323,11 @@ int main(int argc, char **argv)
                 sink.done();
                 return true;
             };
-            res.set_chunked_content_provider("text/event-stream", chunked_content_provider);
+            const auto on_complete = [&](bool) {
+                llama.mutex.unlock();
+            };
+            lock.release();
+            res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
         } });
 
     svr.Get("/model.json", [&llama](const Request &, Response &res)
@@ -1319,8 +1383,12 @@ int main(int argc, char **argv)
 
     svr.set_error_handler([](const Request &, Response &res)
                           {
-        res.set_content("File Not Found", "text/plain");
-        res.status = 404; });
+        if (res.status == 400) {
+            res.set_content("Invalid request", "text/plain");
+        } else {
+            res.set_content("File Not Found", "text/plain");
+            res.status = 404;
+        } });
 
     // set timeouts and change hostname and port
     svr.set_read_timeout(sparams.read_timeout);
@@ -1348,6 +1416,9 @@ int main(int argc, char **argv)
         return 1;
     }
 
+    if (llama.grammar != nullptr) {
+        llama_grammar_free(llama.grammar);
+    }
     llama_backend_free();
 
     return 0;
diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index aa2c4352df294a4e2df58e614a98a38505264b32..97137a6584aa3d6a0741e9689ef0d4dab6a8a804 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -123,7 +123,7 @@ int main(int argc, char ** argv)
         // Evaluate the tokens :
         //---------------------------------
 
-        if ( llama_eval( ctx , tokens_list.data() , tokens_list.size() , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
+        if ( llama_eval( ctx , tokens_list.data() , int(tokens_list.size()) , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
         {
             fprintf( stderr,  "%s : failed to eval\n" , __func__ );
             return 1;
diff --git a/expose.h b/expose.h
index d542dfebfb91ec1efc2b4db3a6ae29a1b1f1d38d..0905bc3ec8bb9a887f75f1b07302c1c298c60059 100644
--- a/expose.h
+++ b/expose.h
@@ -30,6 +30,7 @@ struct load_model_inputs
     const int batch_size;
     const bool f16_kv;
     const bool low_vram;
+    const bool use_mmq;
     const char * executable_path;
     const char * model_filename;
     const char * lora_filename;
@@ -74,7 +75,7 @@ struct generation_inputs
 struct generation_outputs
 {
     int status = -1;
-    char text[16384]; //16kb should be enough for any response
+    char text[24576]; //24kb should be enough for any response
 };
 
 extern std::string executable_path;
diff --git a/ggml-alloc.c b/ggml-alloc.c
new file mode 100644
index 0000000000000000000000000000000000000000..129af8a97ca6a488ecf78c8dbdb98c32a75b9f54
--- /dev/null
+++ b/ggml-alloc.c
@@ -0,0 +1,541 @@
+#include "ggml-alloc.h"
+#include "ggml.h"
+#include <assert.h>
+#include <stdarg.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#define UNUSED(x) (void)(x)
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
+//#define GGML_ALLOCATOR_DEBUG
+
+//#define AT_PRINTF printf
+#define AT_PRINTF(...) ((void)0)
+
+struct hash_node {
+    struct ggml_tensor * t;
+    int n_children;
+    int n_views;
+};
+
+static size_t hash(void * p) {
+    return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE;
+}
+
+static struct hash_node * hash_get(struct hash_node hash_table[], struct ggml_tensor * t) {
+    size_t h = hash(t);
+
+    // linear probing
+    size_t i = h;
+    while (hash_table[i].t != NULL) {
+        if (hash_table[i].t == t) {
+            return &hash_table[i];
+        }
+        i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
+        if (i == h) {
+            // hash table is full
+            GGML_ASSERT(false);
+        }
+    }
+
+    hash_table[i].t = t;
+    return &hash_table[i];
+}
+
+// TODO: GGML_PAD ?
+static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
+    assert(alignment && !(alignment & (alignment - 1))); // power of 2
+    size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
+    return offset + align;
+}
+
+struct free_block {
+    void * addr;
+    size_t size;
+};
+
+#define MAX_FREE_BLOCKS 256
+
+struct ggml_allocr {
+    void * data;
+    size_t size;
+    size_t alignment;
+    int n_free_blocks;
+    struct free_block free_blocks[MAX_FREE_BLOCKS];
+    struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
+    size_t max_size;
+    bool measure;
+
+#ifdef GGML_ALLOCATOR_DEBUG
+    struct ggml_tensor * allocated_tensors[1024];
+#endif
+};
+
+#ifdef GGML_ALLOCATOR_DEBUG
+static void add_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
+    for (int i = 0; i < 1024; i++) {
+        if (alloc->allocated_tensors[i] == NULL) {
+            alloc->allocated_tensors[i] = tensor;
+            return;
+        }
+    }
+    GGML_ASSERT(!"out of allocated_tensors");
+}
+static void remove_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
+    for (int i = 0; i < 1024; i++) {
+        if (alloc->allocated_tensors[i] == tensor ||
+            (alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {
+            alloc->allocated_tensors[i] = NULL;
+            return;
+        }
+    }
+    printf("tried to free tensor %s not found\n", tensor->name);
+    GGML_ASSERT(!"tensor not found");
+}
+#endif
+
+
+static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+    return ggml_nbytes(tensor);
+
+    UNUSED(alloc);
+}
+
+void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+    size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
+    size = aligned_offset(NULL, size, alloc->alignment);
+
+    AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
+
+    size_t max_avail = 0;
+
+    // find the best fitting free block
+    int best_fit_block = -1;
+    size_t best_fit_size = SIZE_MAX;
+    for (int i = 0; i < alloc->n_free_blocks; i++) {
+        struct free_block * block = &alloc->free_blocks[i];
+        max_avail = MAX(max_avail, block->size);
+        if (block->size >= size && block->size <= best_fit_size) {
+            best_fit_block = i;
+            best_fit_size = block->size;
+        }
+    }
+
+    AT_PRINTF("block %d\n", best_fit_block);
+
+    if (best_fit_block == -1) {
+        fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
+                __func__, size, max_avail);
+        GGML_ASSERT(!"not enough space in the buffer");
+        return;
+    }
+    struct free_block * block = &alloc->free_blocks[best_fit_block];
+    void * addr = block->addr;
+    block->addr = (char*)block->addr + size;
+    block->size -= size;
+    if (block->size == 0) {
+        // remove block if empty
+        alloc->n_free_blocks--;
+        for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
+            alloc->free_blocks[j] = alloc->free_blocks[j+1];
+        }
+    }
+
+    tensor->data = addr;
+
+#ifdef GGML_ALLOCATOR_DEBUG
+    add_allocated_tensor(alloc, tensor);
+    size_t cur_max = (char*)addr - (char*)alloc->data + size;
+    if (cur_max > alloc->max_size) {
+        printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
+        for (int i = 0; i < 1024; i++) {
+            if (alloc->allocated_tensors[i]) {
+                printf("%s (%.2f MB) ", alloc->allocated_tensors[i]->name, ggml_nbytes(alloc->allocated_tensors[i]) / 1024.0 / 1024.0);
+            }
+        }
+        printf("\n");
+    }
+#endif
+
+    alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size);
+}
+
+// this is a very naive implementation, but for our case the number of free blocks should be very small
+static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+    void * ptr = tensor->data;
+
+    if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) {
+        // the tensor was not allocated in this buffer
+        // this can happen because the graph allocator will try to free weights and other tensors from different buffers
+        // the easiest way to deal with this is just to ignore it
+        return;
+    }
+
+    size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
+    size = aligned_offset(NULL, size, alloc->alignment);
+    AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks);
+
+#ifdef GGML_ALLOCATOR_DEBUG
+    remove_allocated_tensor(alloc, tensor);
+#endif
+
+    // see if we can merge with an existing block
+    for (int i = 0; i < alloc->n_free_blocks; i++) {
+        struct free_block * block = &alloc->free_blocks[i];
+        // check if ptr is at the end of the block
+        if ((char*)block->addr + block->size == ptr) {
+            block->size += size;
+            // check if we can merge with the next block
+            if (i < alloc->n_free_blocks - 1 && (char*)block->addr + block->size == alloc->free_blocks[i+1].addr) {
+                block->size += alloc->free_blocks[i+1].size;
+                alloc->n_free_blocks--;
+                for (int j = i+1; j < alloc->n_free_blocks; j++) {
+                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
+                }
+            }
+            return;
+        }
+        // check if ptr is at the beginning of the block
+        if ((char*)ptr + size == block->addr) {
+            block->addr = ptr;
+            block->size += size;
+            // check if we can merge with the previous block
+            if (i > 0 && (char*)alloc->free_blocks[i-1].addr + alloc->free_blocks[i-1].size == block->addr) {
+                alloc->free_blocks[i-1].size += block->size;
+                alloc->n_free_blocks--;
+                for (int j = i; j < alloc->n_free_blocks; j++) {
+                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
+                }
+            }
+            return;
+        }
+    }
+    // otherwise, add a new block
+    GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
+    // insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
+    int insert_pos = 0;
+    while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].addr < ptr) {
+        insert_pos++;
+    }
+    // shift all blocks from insert_pos onward to make room for the new block
+    for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
+        alloc->free_blocks[i] = alloc->free_blocks[i-1];
+    }
+    // insert the new block
+    alloc->free_blocks[insert_pos].addr = ptr;
+    alloc->free_blocks[insert_pos].size = size;
+    alloc->n_free_blocks++;
+}
+
+void ggml_allocr_reset(struct ggml_allocr * alloc) {
+    alloc->n_free_blocks = 1;
+    size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment);
+    alloc->free_blocks[0].addr = (char *)alloc->data + align_offset;
+    alloc->free_blocks[0].size = alloc->size - align_offset;
+}
+
+struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) {
+    struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
+
+    *alloc = (struct ggml_allocr){
+        /*.data          = */ data,
+        /*.size          = */ size,
+        /*.alignment     = */ alignment,
+        /*.n_free_blocks = */ 0,
+        /*.free_blocks   = */ {{0}},
+        /*.hash_table    = */ {{0}},
+        /*.max_size      = */ 0,
+        /*.measure       = */ false,
+#ifdef GGML_ALLOCATOR_DEBUG
+        /*.allocated_tensors = */ = {0},
+#endif
+    };
+
+    ggml_allocr_reset(alloc);
+
+    return alloc;
+}
+
+// address and size of the buffer when measuring
+// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers
+static void * const MEASURE_BASE_ADDR = (void *) 0x1000;
+static const size_t MEASURE_MAX_SIZE  = 1ULL<<40; // 1 TB
+
+struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
+    struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
+
+    *alloc = (struct ggml_allocr){
+        /*.data          = */ MEASURE_BASE_ADDR,
+        /*.size          = */ MEASURE_MAX_SIZE,
+        /*.alignment     = */ alignment,
+        /*.n_free_blocks = */ 0,
+        /*.free_blocks   = */ {{0}},
+        /*.hash_table    = */ {{0}},
+        /*.max_size      = */ 0,
+        /*.measure       = */ true,
+#ifdef GGML_ALLOCATOR_DEBUG
+        /*.allocated_tensors = */ = {0},
+#endif
+    };
+
+    ggml_allocr_reset(alloc);
+
+    return alloc;
+}
+
+void ggml_allocr_free(struct ggml_allocr * alloc) {
+    free(alloc);
+}
+
+bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
+    return alloc->measure;
+}
+
+//////////// compute graph allocator
+
+static bool ggml_is_view(struct ggml_tensor * t) {
+    return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE ||
+           t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
+}
+
+static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
+    if (a->type != b->type) {
+        return false;
+    }
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (a->ne[i] != b->ne[i]) {
+            return false;
+        }
+        if (a->nb[i] != b->nb[i]) {
+            return false;
+        }
+    }
+    return true;
+}
+
+static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
+    switch (t->op) {
+        case GGML_OP_PERMUTE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_VIEW:
+            return t->src[0];
+        case GGML_OP_CPY:
+            return t->src[1];
+        default:
+            return NULL;
+    }
+}
+
+static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
+    struct ggml_tensor * parent = t;
+    do {
+        parent = get_view_parent(parent);
+    } while (ggml_is_view(parent));
+    return parent;
+}
+
+static bool ggml_op_can_inplace(enum ggml_op op) {
+    switch (op) {
+        case GGML_OP_SCALE:
+        case GGML_OP_DIAG_MASK_ZERO:
+        case GGML_OP_DIAG_MASK_INF:
+        case GGML_OP_ADD:
+        case GGML_OP_ADD1:
+        case GGML_OP_ACC:
+        case GGML_OP_SUB:
+        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+        case GGML_OP_SQR:
+        case GGML_OP_SQRT:
+        case GGML_OP_LOG:
+        case GGML_OP_UNARY:
+        case GGML_OP_ROPE:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_SET:
+        case GGML_OP_SOFT_MAX:
+        case GGML_OP_CONT:
+            return true;
+
+        default:
+            return false;
+    }
+}
+
+static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) {
+    struct hash_node * ht = alloc->hash_table;
+    if (node->data == NULL) {
+        if (ggml_is_view(node)) {
+            size_t offset;
+            switch(node->op) {
+                case GGML_OP_VIEW:
+                    memcpy(&offset, node->op_params, sizeof(size_t));
+                    node->data = (char *) node->src[0]->data + offset;
+                    break;
+                case GGML_OP_PERMUTE:
+                case GGML_OP_RESHAPE:
+                case GGML_OP_TRANSPOSE:
+                    node->data = node->src[0]->data;
+                    break;
+                case GGML_OP_CPY:
+                    node->data = node->src[1]->data;
+                    break;
+                default:
+                    GGML_ASSERT(!"unknown view op");
+                    break;
+            }
+        } else {
+            // see if we can reuse a parent's buffer (inplace)
+            if (ggml_op_can_inplace(node->op)) {
+                for (int i = 0; i < GGML_MAX_SRC; i++) {
+                    struct ggml_tensor * parent = node->src[i];
+                    if (parent == NULL) {
+                        break;
+                    }
+                    struct hash_node * p_hn = hash_get(ht, parent);
+                    if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
+                        if (ggml_is_view(parent)) {
+                            struct ggml_tensor * view_src = get_view_source(parent);
+                            struct hash_node * view_src_hn = hash_get(ht, view_src);
+                            if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
+                                // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
+                                // the parent's data that it will need later (same layout requirement). the problem is that then
+                                // we cannot free the tensor because the original address of the allocation is lost.
+                                // adding a view_src pointer to the tensor would solve this and simplify the code dealing with views
+                                // for now, we only reuse the parent's data if the offset is zero (view_src->data == parent->data)
+                                AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
+                                node->data = parent->data;
+                                return;
+                            }
+                        }
+                        else {
+                            AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
+                            node->data = parent->data;
+                        }
+                        return;
+                    }
+                }
+            }
+            ggml_allocr_alloc(alloc, node);
+        }
+    }
+}
+
+static size_t ggml_allocator_alloc_graph_tensors_n(
+    struct ggml_allocr * alloc,
+    struct ggml_cgraph ** graphs, int n_graphs,
+    struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
+
+    // reset hash table
+    struct hash_node * ht = alloc->hash_table;
+    memset(ht, 0, sizeof(struct hash_node) * GGML_GRAPH_HASHTABLE_SIZE);
+
+    // count number of children and views
+    for (int g = 0; g < n_graphs; g++) {
+        struct ggml_cgraph * gf = graphs[g];
+        for (int i = 0; i < gf->n_nodes; i++) {
+            struct ggml_tensor * node = gf->nodes[i];
+
+            if (ggml_is_view(node)) {
+                struct ggml_tensor * view_src = get_view_source(node);
+                hash_get(ht, view_src)->n_views += 1;
+            }
+
+            for (int j = 0; j < GGML_MAX_SRC; j++) {
+                struct ggml_tensor * parent = node->src[j];
+                if (parent == NULL) {
+                    break;
+                }
+                hash_get(ht, parent)->n_children += 1;
+            }
+        }
+    }
+
+    // allocate tensors
+    for (int g = 0; g < n_graphs; g++) {
+        struct ggml_cgraph * gf = graphs[g];
+        AT_PRINTF("####### graph %d/%d\n", g, n_graphs);
+        // graph inputs are allocated first to ensure that they are not overwritten by each other
+        if (inputs != NULL && inputs[g] != NULL) {
+            for (int i = 0; inputs[g][i] != NULL; i++) {
+                struct ggml_tensor * input = inputs[g][i];
+                AT_PRINTF("input: %s\n", input->name);
+                allocate_node(alloc, input);
+            }
+        }
+        for (int i = 0; i < gf->n_nodes; i++) {
+            struct ggml_tensor * node = gf->nodes[i];
+
+            // allocate parents (leafs)
+            for (int j = 0; j < GGML_MAX_SRC; j++) {
+                struct ggml_tensor * parent = node->src[j];
+                if (parent == NULL) {
+                    break;
+                }
+                allocate_node(alloc, parent);
+            }
+
+            // allocate node
+            allocate_node(alloc, node);
+
+            AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
+            for (int j = 0; j < GGML_MAX_SRC; j++) {
+                struct ggml_tensor * parent = node->src[j];
+                if (parent == NULL) {
+                    break;
+                }
+                AT_PRINTF("%s", parent->name);
+                if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
+                    AT_PRINTF(", ");
+                }
+            }
+            AT_PRINTF("\n");
+
+            // update parents
+            for (int j = 0; j < GGML_MAX_SRC; j++) {
+                struct ggml_tensor * parent = node->src[j];
+                if (parent == NULL) {
+                    break;
+                }
+                struct hash_node * p_hn = hash_get(ht, parent);
+                p_hn->n_children -= 1;
+
+                //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
+
+                if (p_hn->n_children == 0 && p_hn->n_views == 0) {
+                    if (ggml_is_view(parent)) {
+                        struct ggml_tensor * view_src = get_view_source(parent);
+                        struct hash_node * view_src_hn = hash_get(ht, view_src);
+                        view_src_hn->n_views -= 1;
+                        AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src->n_children, view_src->n_views);
+                        if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
+                            ggml_allocator_free_tensor(alloc, view_src);
+                        }
+                    }
+                    else {
+                        if (parent->data != node->data) {
+                            ggml_allocator_free_tensor(alloc, parent);
+                        }
+                    }
+                }
+            }
+            AT_PRINTF("\n");
+        }
+        // free graph outputs here that wouldn't be freed otherwise because they have no children
+        if (outputs != NULL && outputs[g] != NULL) {
+            for (int i = 0; outputs[g][i] != NULL; i++) {
+                struct ggml_tensor * output = outputs[g][i];
+                AT_PRINTF("output: %s\n", output->name);
+                ggml_allocator_free_tensor(alloc, output);
+            }
+        }
+    }
+
+    return alloc->max_size;
+}
+
+size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
+    return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
+}
diff --git a/ggml-alloc.h b/ggml-alloc.h
new file mode 100644
index 0000000000000000000000000000000000000000..a5ec8f87a94530840ef7c030a6db9c3e9cdef6e0
--- /dev/null
+++ b/ggml-alloc.h
@@ -0,0 +1,22 @@
+#pragma once
+
+#include "ggml.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+
+GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
+GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
+
+GGML_API void   ggml_allocr_free(struct ggml_allocr * alloc);
+GGML_API bool   ggml_allocr_is_measure(struct ggml_allocr * alloc);
+GGML_API void   ggml_allocr_reset(struct ggml_allocr * alloc);
+GGML_API void   ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);
+GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
+
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 30f527cca94a00bd7e0efb01a3a4c1e974d39d82..856ebf1c1ae159876821d383ce73ac5767814ec1 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -52,13 +52,41 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
     } while (0)
 #endif // CUDART_VERSION >= 11
 
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
 typedef half dfloat; // dequantize float
 typedef half2 dfloat2;
 #else
 typedef float dfloat; // dequantize float
 typedef float2 dfloat2;
-#endif //GGML_CUDA_DMMV_F16
+#endif //GGML_CUDA_F16
+
+static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
+    const uint16_t * x16 = (uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+
+    int x32 = 0;
+    x32 |= x16[0] <<  0;
+    x32 |= x16[1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) {
+    const uint16_t * x16 = (uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+
+    int x32 = 0;
+    x32 |= x16[0] <<  0;
+    x32 |= x16[1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) {
+    return *((int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) {
+    return *((int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
 
 typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
 typedef void (*to_fp32_cuda_t)(const void * __restrict__ x, float * __restrict__ y, int k, cudaStream_t stream);
@@ -87,8 +115,7 @@ static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0
 #define QR4_1 2
 #define QI4_1 (QK4_1 / (4 * QR4_1))
 typedef struct {
-    half    d;              // delta
-    half    m;              // min
+    half2   dm;             // dm.x = delta, dm.y = min
     uint8_t qs[QK4_1 / 2];  // nibbles / quants
 } block_q4_1;
 static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
@@ -107,8 +134,7 @@ static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5
 #define QR5_1 2
 #define QI5_1 (QK5_1 / (4 * QR5_1))
 typedef struct {
-    half d;                 // delta
-    half m;                 // min
+    half2 dm;               // dm.x = delta, dm.y = min
     uint8_t qh[4];          // 5-th bit of quants
     uint8_t qs[QK5_1 / 2];  // nibbles / quants
 } block_q5_1;
@@ -127,13 +153,19 @@ static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 blo
 #define QR8_1 1
 #define QI8_1 (QK8_1 / (4 * QR8_1))
 typedef struct {
-    half    d;              // delta
-    half    s;              // unquantized sum
+    half2   ds;             // ds.x = delta, ds.y = sum
     int8_t  qs[QK8_0];      // quants
 } block_q8_1;
 static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding");
 
-typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs);
+typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
+typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
+typedef void (*load_tiles_cuda_t)(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
+typedef float (*vec_dot_q_mul_mat_cuda_t)(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);
 
 //================================= k-quants
 
@@ -150,8 +182,7 @@ typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_
 typedef struct {
     uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
     uint8_t qs[QK_K/4];      // quants
-    half d;                  // super-block scale for quantized scales
-    half dmin;               // super-block scale for quantized mins
+    half2 dm;                // super-block scale for quantized scales/mins
 } block_q2_K;
 static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
 
@@ -180,8 +211,7 @@ typedef struct {
 static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
 #else
 typedef struct {
-    half d;                    // super-block scale for quantized scales
-    half dmin;                 // super-block scale for quantized mins
+    half2 dm;                  // super-block scale for quantized scales/mins
     uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
     uint8_t qs[QK_K/2];        // 4--bit quants
 } block_q4_K;
@@ -200,11 +230,10 @@ typedef struct {
 static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
 #else
 typedef struct {
-    half d;               // super-block scale for quantized scales
-    half dmin;            // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE];   // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
+    half2 dm;                     // super-block scale for quantized scales/mins
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uint8_t qh[QK_K/8];           // quants, high bit
+    uint8_t qs[QK_K/2];           // quants, low 4 bits
 } block_q5_K;
 static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
 #endif
@@ -233,6 +262,10 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
 #define CUDA_QUANTIZE_BLOCK_SIZE 256
 #define CUDA_DEQUANTIZE_BLOCK_SIZE 256
 
+#ifndef GGML_CUDA_MMQ_Y
+#define GGML_CUDA_MMQ_Y 64
+#endif // GGML_CUDA_MMQ_Y
+
 // dmmv = dequantize_mul_mat_vec
 #ifndef GGML_CUDA_DMMV_X
 #define GGML_CUDA_DMMV_X 32
@@ -367,33 +400,33 @@ static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const in
     v.x = vui & 0xF;
     v.y = vui >> 4;
 
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
     v = __hsub2(v, {8.0f, 8.0f});
     v = __hmul2(v, {d, d});
 #else
     v.x = (v.x - 8.0f) * d;
     v.y = (v.y - 8.0f) * d;
-#endif // GGML_CUDA_DMMV_F16
+#endif // GGML_CUDA_F16
 }
 
 static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
     const block_q4_1 * x = (const block_q4_1 *) vx;
 
-    const dfloat d = x[ib].d;
-    const dfloat m = x[ib].m;
+    const dfloat d = x[ib].dm.x;
+    const dfloat m = x[ib].dm.y;
 
     const int vui = x[ib].qs[iqs];
 
     v.x = vui & 0xF;
     v.y = vui >> 4;
 
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
     v = __hmul2(v, {d, d});
     v = __hadd2(v, {m, m});
 #else
     v.x = (v.x * d) + m;
     v.y = (v.y * d) + m;
-#endif // GGML_CUDA_DMMV_F16
+#endif // GGML_CUDA_F16
 }
 
 static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
@@ -410,20 +443,20 @@ static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const in
     v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
     v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
 
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
     v = __hsub2(v, {16.0f, 16.0f});
     v = __hmul2(v, {d, d});
 #else
     v.x = (v.x - 16.0f) * d;
     v.y = (v.y - 16.0f) * d;
-#endif // GGML_CUDA_DMMV_F16
+#endif // GGML_CUDA_F16
 }
 
 static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
     const block_q5_1 * x = (const block_q5_1 *) vx;
 
-    const dfloat d = x[ib].d;
-    const dfloat m = x[ib].m;
+    const dfloat d = x[ib].dm.x;
+    const dfloat m = x[ib].dm.y;
 
     uint32_t qh;
     memcpy(&qh, x[ib].qh, sizeof(qh));
@@ -434,13 +467,13 @@ static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const in
     v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
     v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
 
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
     v = __hmul2(v, {d, d});
     v = __hadd2(v, {m, m});
 #else
     v.x = (v.x * d) + m;
     v.y = (v.y * d) + m;
-#endif // GGML_CUDA_DMMV_F16
+#endif // GGML_CUDA_F16
 }
 
 static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
@@ -451,12 +484,12 @@ static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const in
     v.x = x[ib].qs[iqs + 0];
     v.y = x[ib].qs[iqs + 1];
 
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
     v = __hmul2(v, {d, d});
 #else
     v.x *= d;
     v.y *= d;
-#endif // GGML_CUDA_DMMV_F16
+#endif // GGML_CUDA_F16
 }
 
 //================================== k-quants
@@ -475,8 +508,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, float
     const uint8_t q = x[i].qs[32*n + l];
     float * y = yy + i*QK_K + 128*n;
 
-    float dall = x[i].d;
-    float dmin = x[i].dmin;
+    float dall = x[i].dm.x;
+    float dmin = x[i].dm.y;
     y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
     y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
     y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
@@ -486,8 +519,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, float
     const int il = tid%16;  // 0...15
     const uint8_t q = x[i].qs[il] >> (2*is);
     float * y = yy + i*QK_K + 16*is + il;
-    float dall = x[i].d;
-    float dmin = x[i].dmin;
+    float dall = x[i].dm.x;
+    float dmin = x[i].dm.y;
     y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
     y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
 #endif
@@ -573,8 +606,8 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, float
 
     float * y = yy + i*QK_K + 64*il + n*ir;
 
-    const float dall = x[i].d;
-    const float dmin = x[i].dmin;
+    const float dall = x[i].dm.x;
+    const float dmin = x[i].dm.y;
 
     const uint8_t * q = x[i].qs + 32*il + n*ir;
 
@@ -612,8 +645,8 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, float
 
     float * y = yy + i*QK_K + 64*il + 2*ir;
 
-    const float dall = x[i].d;
-    const float dmin = x[i].dmin;
+    const float dall = x[i].dm.x;
+    const float dmin = x[i].dm.y;
 
     const uint8_t * ql = x[i].qs + 32*il + 2*ir;
     const uint8_t * qh = x[i].qh + 2*ir;
@@ -725,8 +758,8 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
         const float   * y = yy + i * QK_K + y_offset;
         const uint8_t * q = x[i].qs + q_offset;
 
-        const float dall = x[i].d;
-        const float dmin = x[i].dmin;
+        const float dall = x[i].dm.x;
+        const float dmin = x[i].dm.y;
 
         const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
         aux[0] = a[0] & 0x0f0f0f0f;
@@ -768,9 +801,7 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
         uaux[0] = s[0] & 0x0f0f0f0f;
         uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
 
-        const half2 * dh = (const half2 *)&x[i].d;
-
-        const float2 dall = __half22float2(dh[0]);
+        const float2 dall = __half22float2(x[i].dm);
 
         float sum1 = 0, sum2 = 0;
         for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
@@ -948,8 +979,8 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
         const float   * y1 = yy + i*QK_K + y_offset;
         const float   * y2 = y1 + 128;
 
-        const float dall = x[i].d;
-        const float dmin = x[i].dmin;
+        const float dall = x[i].dm.x;
+        const float dmin = x[i].dm.y;
 
         const uint16_t * a = (const uint16_t *)x[i].scales;
         aux[0] = a[im+0] & kmask1;
@@ -1081,8 +1112,8 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
         const float   * y1  = yy + i*QK_K + y_offset;
         const float   * y2  = y1 + 128;
 
-        const float dall = x[i].d;
-        const float dmin = x[i].dmin;
+        const float dall = x[i].dm.x;
+        const float dmin = x[i].dm.y;
 
         const uint16_t * a = (const uint16_t *)x[i].scales;
         aux[0] = a[im+0] & kmask1;
@@ -1270,19 +1301,23 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs,
     v.y = x[ib + iqs + 1];
 }
 
-static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int ndata, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) {
+    const int ix = blockDim.x*blockIdx.x + threadIdx.x;
 
-    if (i >= k) {
+    if (ix >= kx_padded) {
         return;
     }
 
+    const int iy = blockDim.y*blockIdx.y + threadIdx.y;
+
+    const int i_padded = iy*kx_padded + ix;
+
     block_q8_1 * y = (block_q8_1 *) vy;
 
-    const int ib = i / QK8_1; // block index
-    const int iqs = i % QK8_1; // quant index
+    const int ib = i_padded / QK8_1; // block index
+    const int iqs = i_padded % QK8_1; // quant index
 
-    const float xi = i < ndata ? x[i] : 0.0f;
+    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
     float amax = fabsf(xi);
     float sum = xi;
 
@@ -1301,8 +1336,8 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
         return;
     }
 
-    y[ib].d = d;
-    y[ib].s = sum;
+    y[ib].ds.x = d;
+    y[ib].ds.y = sum;
 }
 
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
@@ -1326,485 +1361,1963 @@ static __global__ void dequantize_block(const void * __restrict__ vx, float * __
     y[iybs + iqs + y_offset] = v.y;
 }
 
-static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
+// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
+// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
+
+#define VDR_Q4_0_Q8_1_MMVQ 2
+#define VDR_Q4_0_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
+    const int * v, const int * u, const float & d4, const half2 & ds8) {
 
-    int vi;
-    memcpy(&vi,  &bq4_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
-    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
-    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_0)]);
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
 
-    const float d = __half2float(bq4_0->d) * __half2float(bq8_1->d);
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
 
-    // subtract 8 from each quantized value
-    const int vi0 = __vsub4((vi >> 0) & 0x0F0F0F0F, 0x08080808);
-    const int vi1 = __vsub4((vi >> 4) & 0x0F0F0F0F, 0x08080808);
+        // SIMD dot product of quantized values
+        sumi = __dp4a(vi0, u[2*i+0], sumi);
+        sumi = __dp4a(vi1, u[2*i+1], sumi);
+    }
 
-    // SIMD dot product of quantized values
-    int sumi = __dp4a(vi0, ui0, 0);
-    sumi     = __dp4a(vi1, ui1, sumi);
+    const float2 ds8f = __half22float2(ds8);
 
-    return sumi*d;
+    // second part effectively subtracts 8 from each quant value
+    return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
 #else
     return 0.0f; // only to satisfy the compiler
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
-static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
+#define VDR_Q4_1_Q8_1_MMVQ 2
+#define VDR_Q4_1_Q8_1_MMQ  4
 
-    const int vi  = *((int *) &bq4_1->qs[sizeof(int) * (iqs + 0)]);
-    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
-    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_1)]);
+template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
+    const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
 
-    const float d = __half2float(bq4_1->d) * __half2float(bq8_1->d);
-    const float m = bq4_1->m;
-    const float s = bq8_1->s;
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
 
-    const int vi0 = (vi >> 0) & 0x0F0F0F0F;
-    const int vi1 = (vi >> 4) & 0x0F0F0F0F;
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
 
-    // SIMD dot product of quantized values
-    int sumi = __dp4a(vi0, ui0, 0);
-    sumi     = __dp4a(vi1, ui1, sumi);
+        // SIMD dot product of quantized values
+        sumi = __dp4a(vi0, u[2*i+0], sumi);
+        sumi = __dp4a(vi1, u[2*i+1], sumi);
+    }
 
-    return sumi*d + m*s / QI4_1; // scale sum by QI4_1 because there are QI4_1 threads working on this block
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm4, ds8));
+    const float d4d8 = tmp.x;
+    const float m4s8 = tmp.y;
+#else
+    const float2 dm4f = __half22float2(dm4);
+    const float2 ds8f = __half22float2(ds8);
+    const float d4d8 = dm4f.x * ds8f.x;
+    const float m4s8 = dm4f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
+    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
 #else
     return 0.0f; // only to satisfy the compiler
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
-static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
+#define VDR_Q5_0_Q8_1_MMVQ 2
+#define VDR_Q5_0_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
+    const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
+
 #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+    }
+
+    const float2 ds8f = __half22float2(ds8);
 
-    int qs;
-    memcpy(&qs, &bq5_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
-    const int qh0 = bq5_0->qh[iqs/2 + 0] >> 4*(iqs%2);
-    const int qh1 = bq5_0->qh[iqs/2 + 2] >> 4*(iqs%2);
-    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
-    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_0)]);
-
-    const float d = __half2float(bq5_0->d) * __half2float(bq8_1->d);
-
-    int vi0 = (qs  >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
-    vi0    |= (qh0 <<  4) & 0x00000010; // 1 ->  5
-    vi0    |= (qh0 << 11) & 0x00001000; // 2 -> 13
-    vi0    |= (qh0 << 18) & 0x00100000; // 3 -> 21
-    vi0    |= (qh0 << 25) & 0x10000000; // 4 -> 29
-    vi0     = __vsub4(vi0,  0x10101010); // subtract 16 from quantized values
-    int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
-
-    int vi1 = (qs  >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
-    vi1    |= (qh1 <<  4) & 0x00000010; // 1 ->  5
-    vi1    |= (qh1 << 11) & 0x00001000; // 2 -> 13
-    vi1    |= (qh1 << 18) & 0x00100000; // 3 -> 21
-    vi1    |= (qh1 << 25) & 0x10000000; // 4 -> 29
-    vi1     = __vsub4(vi1,  0x10101010); // subtract 16 from quantized values
-    sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
-
-    return sumi*d;
+    // second part effectively subtracts 16 from each quant value
+    return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
 #else
     return 0.0f; // only to satisfy the compiler
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
-static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
+#define VDR_Q5_1_Q8_1_MMVQ 2
+#define VDR_Q5_1_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
+    const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
+
 #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm5, ds8));
+    const float d5d8 = tmp.x;
+    const float m5s8 = tmp.y;
+#else
+    const float2 dm5f = __half22float2(dm5);
+    const float2 ds8f = __half22float2(ds8);
+    const float d5d8 = dm5f.x * ds8f.x;
+    const float m5s8 = dm5f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
+    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
 
-    const int qs  = *((int *) &bq5_1->qs[sizeof(int) * (iqs + 0)]);
-    const int qh0 = bq5_1->qh[iqs/2 + 0] >> 4*(iqs%2);
-    const int qh1 = bq5_1->qh[iqs/2 + 2] >> 4*(iqs%2);
-    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
-    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_1)]);
-
-    const float d = __half2float(bq5_1->d) * __half2float(bq8_1->d);
-    const float m = bq5_1->m;
-    const float s = bq8_1->s;
-
-    int vi0 = (qs  >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
-    vi0    |= (qh0 <<  4) & 0x00000010; // 1 ->  5
-    vi0    |= (qh0 << 11) & 0x00001000; // 2 -> 13
-    vi0    |= (qh0 << 18) & 0x00100000; // 3 -> 21
-    vi0    |= (qh0 << 25) & 0x10000000; // 4 -> 29
-    int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
-
-    int vi1 = (qs  >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
-    vi1    |= (qh1 <<  4) & 0x00000010; // 1 ->  5
-    vi1    |= (qh1 << 11) & 0x00001000; // 2 -> 13
-    vi1    |= (qh1 << 18) & 0x00100000; // 3 -> 21
-    vi1    |= (qh1 << 25) & 0x10000000; // 4 -> 29
-    sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
-
-    return sumi*d + m*s / QI5_1; // scale sum by QI5_1 because there are QI5_1 threads working on this block
 #else
     return 0.0f; // only to satisfy the compiler
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
-static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
+#define VDR_Q8_0_Q8_1_MMVQ 2
+#define VDR_Q8_0_Q8_1_MMQ 8
 
-    int vi;
-    memcpy(&vi,  &bq8_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
-    const int ui = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
+    const int * v, const int * u, const float & d8_0, const float & d8_1) {
 
-    const float d = __half2float(bq8_0->d) * __half2float(bq8_1->d);
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
 
-    // SIMD dot product of quantized values
-    int sumi = __dp4a(vi, ui, 0);
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = __dp4a(v[i], u[i], sumi);
+    }
 
-    return sumi*d;
+    return d8_0*d8_1 * sumi;
 #else
     return 0.0f; // only to satisfy the compiler
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
+    const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
 
 #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
+    int sumi = 0;
 
-    const int bq8_offset = QR2_K * (iqs / QI8_1);
-    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = __dp4a(v[i], u[i], sumi);
+    }
 
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm8, ds8));
+    const float d8d8 = tmp.x;
+    const float m8s8 = tmp.y;
+#else
+    const float2 dm8f = __half22float2(dm8);
+    const float2 ds8f = __half22float2(ds8);
+    const float d8d8 = dm8.x * ds8.x;
+    const float m8s8 = dm8.y * ds8.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
+    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
 
-    const float    d = bq2_K->d;
-    const float dmin = bq2_K->dmin;
+#define VDR_Q2_K_Q8_1_MMVQ 1
+#define VDR_Q2_K_Q8_1_MMQ  2
 
-    const int v = *((int *) &bq2_K->qs[sizeof(int) * iqs]);
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
+    const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const half2 & dm2, const float * __restrict__ d8) {
 
-    for (int i = 0; i < QR2_K; ++i) {
-        const int sc = bq2_K->scales[scale_offset + 2*i];
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
 
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        const float d8i = bq8i->d;
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++i) {
+        const int sc = scales[2*i];
 
         const int vi = (v >> (2*i)) & 0x03030303;
-        const int ui = *((int*) &bq8i->qs[sizeof(int) * (iqs % QI8_1)]);
 
-        sumf_d += d8i * (__dp4a(vi,         ui, 0) * (sc & 0xF)); // SIMD dot product
-        sumf_m += d8i * (__dp4a(0x01010101, ui, 0) * (sc >>  4)); // multiply constant q2_K part with sum of q8_1 values
+        sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+        sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
     }
 
-    return d*sumf_d - dmin*sumf_m;
+    const float2 dm2f = __half22float2(dm2);
+
+    return dm2f.x*sumf_d - dm2f.y*sumf_m;
 #else
     return 0.0f; // only to satisfy the compiler
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const half2 & dm2, const float & d8) {
 
 #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
+    int sumi_d = 0;
+    int sumi_m = 0;
 
-    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
-    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+#pragma unroll
+    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
+        int sumi_d_sc = 0;
 
-    float sumf = 0.0f;
+        const int sc = scales[i0 / (QI8_1/2)];
 
-    const float d = bq3_K->d;
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+
+#pragma unroll
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
+            sumi_m    = __dp4a(m,    u[i], sumi_m); // multiply sum of q8_1 values with m
+        }
+
+        sumi_d += sumi_d_sc * (sc & 0xF);
+    }
 
-    int vl;
-    memcpy(&vl, &bq3_K->qs[sizeof(int) * iqs], sizeof(int));
+    const float2 dm2f = __half22float2(dm2);
 
-    int vh;
-    memcpy(&vh, &bq3_K->hmask[sizeof(int) * (iqs % (QI3_K/2))], sizeof(int));
-    vh = ~vh; // invert the mask so that a 0/1 results in 4/0 being subtracted
-    vh >>= bq8_offset;
+    return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m);
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q3_K_Q8_1_MMVQ 1
+#define VDR_Q3_K_Q8_1_MMQ  2
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
+    const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const int & scale_offset, const float & d3, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf = 0.0f;
 
+#pragma unroll
     for (int i = 0; i < QR3_K; ++i) {
         const int isc = scale_offset + 2*i;
 
         const int isc_low = isc % (QK_K/32);
         const int sc_shift_low = 4 * (isc / (QK_K/32));
-        const int sc_low  = (bq3_K->scales[isc_low] >> sc_shift_low) & 0xF;
+        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
 
         const int isc_high = isc % (QK_K/64);
         const int sc_shift_high = 2 * (isc / (QK_K/64));
-        const int sc_high = ((bq3_K->scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
+        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
 
         const int sc = (sc_low | sc_high) - 32;
 
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        const int ui = *((int*) &bq8i->qs[sizeof(int) * (iqs % QI8_1)]);
-        const float d8i = bq8i->d;
-
         const int vil = (vl >> (2*i)) & 0x03030303;
 
         const int vih = ((vh >> i) << 2) & 0x04040404;
 
         const int vi = __vsubss4(vil, vih);
 
-        sumf += d8i * (__dp4a(vi, ui, 0) * sc); // SIMD dot product
+        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
     }
 
-    return d*sumf;
+    return d3 * sumf;
 #else
     return 0.0f; // only to satisfy the compiler
 #endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
+    const float & d3, const float & d8) {
 
 #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+    int sumi = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
+        int sumi_sc = 0;
+
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product
+        }
+
+        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
+    }
+
+    return d3*d8 * sumi;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q4_K_Q8_1_MMVQ 2
+#define VDR_Q4_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
 
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     float sumf_d = 0.0f;
     float sumf_m = 0.0f;
 
-#ifndef GGML_QKK_64
+#pragma unroll
+    for (int i = 0; i < QR4_K; ++i) {
+        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
+        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
 
-    // iqs is in 0...15. bq8_offset = 2 * (iqs/4) -> bq8_offset = 0, 2, 4, 6
-    const int bq8_offset = QR4_K * (iqs / (QI8_1/2));
+        const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
+        const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u
 
-    const float    d = bq4_K->d;
-    const float dmin = bq4_K->dmin;
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
+    }
 
-    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
-    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
-    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
-    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
+    const float2 dm4f = __half22float2(dm4);
 
-    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * (iqs%4));
-    const int v1 = q4[0];
-    const int v2 = q4[4];
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
 
-    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
-    uint16_t aux[2];
-    const int j = bq8_offset/2;
-    if (j < 2) {
-        aux[0] = scales[j+0] & 0x3f3f;
-        aux[1] = scales[j+2] & 0x3f3f;
-    } else {
-        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
-        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+// also used for q5_K
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i0 = 0; i0 < VDR_Q4_K_Q8_1_MMQ; i0 += (QI8_1/QR4_K)) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int i = i0; i < i0 + (QI8_1/QR4_K); ++i) {
+            sumi_d = __dp4a(v[2*i+0], u[2*i+0], sumi_d); // SIMD dot product
+            sumi_d = __dp4a(v[2*i+1], u[2*i+1], sumi_d); // SIMD dot product
+        }
+
+        const float2 ds8f = __half22float2(ds8[i0 / 4]);
+
+        sumf_d += ds8f.x * (sc[i0/4] * sumi_d);
+        sumf_m += ds8f.y *   m[i0/4]; // sum of q8_1 block * q4_K min val
     }
-    const uint8_t * sc = (const uint8_t *)aux;
-    const uint8_t * m  = sc + 2;
 
-    for (int i = 0; i < QR4_K; ++i) {
+    const float2 dm4f = __half22float2(dm4);
 
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        const float d8i = bq8i->d;
-        const int * q8 = (const int *)bq8i->qs + (iqs%4);
-        const int ui1 = q8[0];
-        const int ui2 = q8[4];
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q5_K_Q8_1_MMVQ 2
+#define VDR_Q5_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl(
+    const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
+        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
+        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
+
+        const int v0i = vl0i | vh0i;
+        const int v1i = vl1i | vh1i;
+
+        const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
+        const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);
+
+    }
+
+    const float2 dm5f = __half22float2(dm5);
+
+    return dm5f.x*sumf_d - dm5f.y*sumf_m;
+
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q6_K_Q8_1_MMVQ 1
+#define VDR_Q6_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
+    const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
+    const float & d, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        const int sc = scales[4*i];
+
+        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
+
+        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
+
+        const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
+
+        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d*sumf;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
+    const float & d6, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+
+#pragma unroll
+    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
+        int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
+
+#pragma unroll
+        for (int i = i0; i < i0 + 2; ++i) {
+            sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
+            sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
+
+            sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
+            sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
+        }
+
+        sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
+    }
+
+    return d6 * sumf_d;
+
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
+
+    int v[VDR_Q4_0_Q8_1_MMVQ];
+    int u[2*VDR_Q4_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
+        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
+}
+
+static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int  tile_x_qs[GGML_CUDA_MMQ_Y * (WARP_SIZE)       + GGML_CUDA_MMQ_Y];
+    __shared__ float tile_x_d[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI4_0) + GGML_CUDA_MMQ_Y/QI4_0];
+
+    *x_ql = tile_x_qs;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    __builtin_assume(i_offset >= 0);
+    __builtin_assume(i_offset <  8);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_0;
+    const int kqsx = k % QI4_0;
+
+    const block_q4_0 * bx0 = (block_q4_0 *) vx;
+
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
+    }
+
+//     const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
+//     const int kbxd = k % blocks_per_tile_x_row;
+
+// #pragma unroll
+//     for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI4_0) {
+//         FIXME out-of-bounds
+//         const int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
+
+//         if (i >= GGML_CUDA_MMQ_Y) {
+//             return;
+//         }
+
+//         const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+//         x_dm[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd].x = bxi->d;
+//     }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    __builtin_assume(i >= 0);
+    __builtin_assume(i <  GGML_CUDA_MMQ_Y);
+    __builtin_assume(j >= 0);
+    __builtin_assume(j <  WARP_SIZE);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+    __builtin_assume(k % VDR_Q4_0_Q8_1_MMQ == 0);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const float * x_dmf = (float *) x_dm;
+
+    int u[2*VDR_Q4_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * (2*WARP_SIZE) + kyqs + l];
+        u[2*l+1] = y_qs[j * (2*WARP_SIZE) + kyqs + l + QI4_0];
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
+         y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
+
+    int v[VDR_Q4_1_Q8_1_MMVQ];
+    int u[2*VDR_Q4_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
+        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
+}
+
+static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_qs[GGML_CUDA_MMQ_Y * (WARP_SIZE) +     + GGML_CUDA_MMQ_Y];
+    __shared__ half2 tile_x_dm[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI4_1) + GGML_CUDA_MMQ_Y/QI4_1];
+
+    *x_ql = tile_x_qs;
+    *x_dm = tile_x_dm;
+}
+
+template <bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    __builtin_assume(i_offset >= 0);
+    __builtin_assume(i_offset <  8);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_1;
+    const int kqsx = k % QI4_1;
+
+    const block_q4_1 * bx0 = (block_q4_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI4_1) {
+        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    __builtin_assume(i >= 0);
+    __builtin_assume(i <  GGML_CUDA_MMQ_Y);
+    __builtin_assume(j >= 0);
+    __builtin_assume(j <  WARP_SIZE);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+    __builtin_assume(k % VDR_Q4_1_Q8_1_MMQ == 0);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+
+    int u[2*VDR_Q4_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * (2*WARP_SIZE) + kyqs + l];
+        u[2*l+1] = y_qs[j * (2*WARP_SIZE) + kyqs + l + QI4_1];
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
+         y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
+
+    int vl[VDR_Q5_0_Q8_1_MMVQ];
+    int vh[VDR_Q5_0_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
+        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
+        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
+    }
+
+    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
+}
+
+static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int  tile_x_ql[GGML_CUDA_MMQ_Y * (2*WARP_SIZE)     + GGML_CUDA_MMQ_Y];
+    __shared__ float tile_x_d[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI5_0) + GGML_CUDA_MMQ_Y/QI5_0];
+
+    *x_ql = tile_x_ql;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    __builtin_assume(i_offset >= 0);
+    __builtin_assume(i_offset <  8);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_0;
+    const int kqsx = k % QI5_0;
+
+    const block_q5_0 * bx0 = (block_q5_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
+
+        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
+        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
+        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
+        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
+        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
+        qs0     = __vsubss4(qs0, 0x10101010); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
+        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
+        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
+        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
+        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
+        qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI5_0) {
+        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    __builtin_assume(i >= 0);
+    __builtin_assume(i <  GGML_CUDA_MMQ_Y);
+    __builtin_assume(j >= 0);
+    __builtin_assume(j <  WARP_SIZE);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+    __builtin_assume(k % VDR_Q5_0_Q8_1_MMQ == 0);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    int u[2*VDR_Q5_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * (2*WARP_SIZE) + kyqs + l];
+        u[2*l+1] = y_qs[j * (2*WARP_SIZE) + kyqs + l + QI5_0];
+    }
+
+    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
+
+    int vl[VDR_Q5_1_Q8_1_MMVQ];
+    int vh[VDR_Q5_1_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
+        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
+        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
+    }
+
+    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
+}
+
+static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[GGML_CUDA_MMQ_Y * (2*WARP_SIZE)     + GGML_CUDA_MMQ_Y];
+    __shared__ half2 tile_x_dm[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI5_1) + GGML_CUDA_MMQ_Y/QI5_1];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+}
+
+template <bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    __builtin_assume(i_offset >= 0);
+    __builtin_assume(i_offset <  8);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_1;
+    const int kqsx = k % QI5_1;
+
+    const block_q5_1 * bx0 = (block_q5_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
+
+        int qs0 = (ql >>  0) & 0x0F0F0F0F;
+        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
+        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
+        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
+        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4) & 0x0F0F0F0F;
+        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
+        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
+        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
+        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI5_1) {
+        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    __builtin_assume(i >= 0);
+    __builtin_assume(i <  GGML_CUDA_MMQ_Y);
+    __builtin_assume(j >= 0);
+    __builtin_assume(j <  WARP_SIZE);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+    __builtin_assume(k % VDR_Q5_1_Q8_1_MMQ == 0);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
+
+    int u[2*VDR_Q5_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * (2*WARP_SIZE) + kyqs + l];
+        u[2*l+1] = y_qs[j * (2*WARP_SIZE) + kyqs + l + QI5_1];
+    }
+
+    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
+
+    int v[VDR_Q8_0_Q8_1_MMVQ];
+    int u[VDR_Q8_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
+        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
+        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+    }
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, bq8_1->ds.x);
+}
+
+static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int  tile_x_qs[GGML_CUDA_MMQ_Y * (WARP_SIZE)       + GGML_CUDA_MMQ_Y];
+    __shared__ float tile_x_d[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI8_0) + GGML_CUDA_MMQ_Y/QI8_0];
+
+    *x_ql = tile_x_qs;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    __builtin_assume(i_offset >= 0);
+    __builtin_assume(i_offset <  8);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+
+    const int kbx  = k / QI8_0;
+    const int kqsx = k % QI8_0;
+    float * x_dmf = (float *) x_dm;
+
+    const block_q8_0 * bx0 = (block_q8_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
+        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbx] = bxi->d;
+    }
+
+//     const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
+//     const int kbxd = k % blocks_per_tile_x_row;
+
+// #pragma unroll
+//     for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI8_0) {
+//         FIXME out-of-bounds
+//         const int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
+
+// #if GGML_CUDA_MMQ_Y < 64
+//         if (i >= GGML_CUDA_MMQ_Y) {
+//             return;
+//         }
+// #endif // GGML_CUDA_MMQ_Y < 64
+
+//         const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+//         x_dm[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd].x = bxi->d;
+//     }
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    __builtin_assume(i >= 0);
+    __builtin_assume(i <  GGML_CUDA_MMQ_Y);
+    __builtin_assume(j >= 0);
+    __builtin_assume(j <  WARP_SIZE);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+    __builtin_assume(k % VDR_Q8_0_Q8_1_MMQ == 0);
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
+         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
+
+    const int bq8_offset = QR2_K * (iqs / QI8_1);
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const uint8_t * scales = bq2_K->scales + scale_offset;
+
+    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
+    int    u[QR2_K];
+    float d8[QR2_K];
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++ i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = bq8_1[bq8_offset + i].ds.x;
+    }
+
+    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
+}
+
+static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[GGML_CUDA_MMQ_Y * (WARP_SIZE)       + GGML_CUDA_MMQ_Y];
+    __shared__ half2 tile_x_dm[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI2_K) + GGML_CUDA_MMQ_Y/QI2_K];
+    __shared__ int   tile_x_sc[GGML_CUDA_MMQ_Y * (WARP_SIZE/4)     + GGML_CUDA_MMQ_Y/4];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    __builtin_assume(i_offset >= 0);
+    __builtin_assume(i_offset <  8);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+
+    const int kbx  = k / QI2_K;
+    const int kqsx = k % QI2_K;
+
+    const block_q2_K * bx0 = (block_q2_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI2_K) {
+        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    __builtin_assume(i >= 0);
+    __builtin_assume(i <  GGML_CUDA_MMQ_Y);
+    __builtin_assume(j >= 0);
+    __builtin_assume(j <  WARP_SIZE);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+    __builtin_assume(k % VDR_Q2_K_Q8_1_MMQ == 0);
+
+    const int kbx = k / QI2_K;
+    const int ky  = (k % QI2_K) * QR2_K;
+    const float * y_df = (const float *) y_ds;
+
+    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
+
+    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
+    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
+
+#pragma unroll
+    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
+        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
+    }
+
+    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
+
+    const int index_y = j * (QR2_K*WARP_SIZE) + QR2_K*k;
+    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
+
+    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const float d = bq3_K->d;
+
+    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
+
+    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
+
+    int    u[QR3_K];
+    float d8[QR3_K];
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = bq8_1[bq8_offset + i].ds.x;
+    }
+
+    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
+}
+
+static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[GGML_CUDA_MMQ_Y * (WARP_SIZE)       + GGML_CUDA_MMQ_Y];
+    __shared__ half2 tile_x_dm[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI3_K) + GGML_CUDA_MMQ_Y/QI3_K];
+    __shared__ int   tile_x_qh[GGML_CUDA_MMQ_Y * (WARP_SIZE/2)     + GGML_CUDA_MMQ_Y/2];
+    __shared__ int   tile_x_sc[GGML_CUDA_MMQ_Y * (WARP_SIZE/4)     + GGML_CUDA_MMQ_Y/4];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_qh = tile_x_qh;
+    *x_sc = tile_x_sc;
+}
+
+template <bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    __builtin_assume(i_offset >= 0);
+    __builtin_assume(i_offset <  8);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+
+    const int kbx  = k / QI3_K;
+    const int kqsx = k % QI3_K;
+
+    const block_q3_K * bx0 = (block_q3_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI3_K) {
+        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 2) {
+        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
+
+        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
+
+        const int ksc = k % (QI3_K/4);
+
+        const int ksc_low = ksc % (QI3_K/8);
+        const int shift_low = 4 * (ksc / (QI3_K/8));
+        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
+
+        const int ksc_high = QI3_K/8;
+        const int shift_high = 2 * ksc;
+        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
+
+        const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    __builtin_assume(i >= 0);
+    __builtin_assume(i <  GGML_CUDA_MMQ_Y);
+    __builtin_assume(j >= 0);
+    __builtin_assume(j <  WARP_SIZE);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+    __builtin_assume(k % VDR_Q3_K_Q8_1_MMQ == 0);
+
+    const int kbx  = k / QI3_K;
+    const int ky  = (k % QI3_K) * QR3_K;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * scales = ((int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
+
+    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
+        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
+        const int shift = 2 * ((ky % 32) / 8);
+        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
+
+        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
+        const int vlh = (vh << 2) & 0x04040404;
+
+        v[l] = __vsubss4(vll, vlh);
+    }
+
+    const int index_y = j * (QR3_K*WARP_SIZE) + k*QR3_K;
+    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    int    v[2];
+    int    u[2*QR4_K];
+    float d8[QR4_K];
+
+    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
+    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
+
+    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
+    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
+    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
+    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
+
+    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    v[0] = q4[0];
+    v[1] = q4[4];
+
+    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+    for (int i = 0; i < QR4_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = bq8i->ds.x;
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
+
+#else
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    const uint16_t * a = (const uint16_t *)bq4_K->scales;
+    aux16[0] = a[0] & 0x0f0f;
+    aux16[1] = (a[0] >> 4) & 0x0f0f;
+
+    const float dall = bq4_K->d[0];
+    const float dmin = bq4_K->d[1];
+
+    const float d8_1 = bq8_1[0].ds.x;
+    const float d8_2 = bq8_1[1].ds.x;
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * q4 = (const int *)bq4_K->qs + (iqs/2);
+    const int v1 = q4[0];
+    const int v2 = q4[4];
+
+    const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0));
+    const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
+    const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
+    const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0));
+
+    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
+    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
+
+    return dall * sumf_d - dmin * sumf_m;
+
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+
+#endif
+}
+
+static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[GGML_CUDA_MMQ_Y * (WARP_SIZE)       + GGML_CUDA_MMQ_Y];
+    __shared__ half2 tile_x_dm[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI4_K) + GGML_CUDA_MMQ_Y/QI4_K];
+    __shared__ int   tile_x_sc[GGML_CUDA_MMQ_Y * (WARP_SIZE/8)     + GGML_CUDA_MMQ_Y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    __builtin_assume(i_offset >= 0);
+    __builtin_assume(i_offset <  8);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI4_K; // == k if QK_K == 256
+
+    const block_q4_K * bx0 = (block_q4_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI4_K) {
+        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % GGML_CUDA_MMQ_Y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
+
+        const int * scales = (int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    __builtin_assume(i >= 0);
+    __builtin_assume(i <  GGML_CUDA_MMQ_Y);
+    __builtin_assume(j >= 0);
+    __builtin_assume(j <  WARP_SIZE);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+    __builtin_assume(k % VDR_Q4_K_Q8_1_MMQ == 0);
+
+    int v[QR4_K*VDR_Q4_K_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_K_Q8_1_MMQ; ++l) {
+        v[l + 0]         = (x_ql[i * (WARP_SIZE + 1) + k + l] >> 0) & 0x0F0F0F0F;
+        v[l + (QI4_K/4)] = (x_ql[i * (WARP_SIZE + 1) + k + l] >> 4) & 0x0F0F0F0F;
+    }
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
+
+    const int index_y = j * (QR4_K*WARP_SIZE) + QR4_K*k;
+    return vec_dot_q4_K_q8_1_impl_mmq(v, &y_qs[index_y], sc, sc+8, x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    int   vl[2];
+    int   vh[2];
+    int    u[2*QR5_K];
+    float d8[QR5_K];
+
+    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
+    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
+
+    vl[0] = ql[0];
+    vl[1] = ql[4];
+
+    vh[0] = qh[0] >> bq8_offset;
+    vh[1] = qh[4] >> bq8_offset;
+
+    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = bq8i->ds.x;
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q5_K_q8_1_impl(vl, vh, u, sc, m, bq5_K->dm, d8);
+
+#else
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    const int8_t * s = bq5_K->scales;
+
+    const float d = bq5_K->d;
+
+    const float d8_1 = bq8_1[0].ds.x;
+    const float d8_2 = bq8_1[1].ds.x;
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * ql = (const int *)bq5_K->qs + (iqs/2);
+    const int vl1 = ql[0];
+    const int vl2 = ql[4];
+
+    const int step = 4 * (iqs/2); // 0, 4, 8, 12
+    const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
+    const int in = step%8; // 0, 4, 0, 4
+    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
+
+    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
+    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
+    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
+    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
+
+    const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1])
+                       + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]);
+
+    return d * sumf_d;
+
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+
+#endif
+}
+
+static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[GGML_CUDA_MMQ_Y * (2*WARP_SIZE)     + GGML_CUDA_MMQ_Y];
+    __shared__ half2 tile_x_dm[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI5_K) + GGML_CUDA_MMQ_Y/QI5_K];
+    __shared__ int   tile_x_sc[GGML_CUDA_MMQ_Y * (WARP_SIZE/8)     + GGML_CUDA_MMQ_Y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    __builtin_assume(i_offset >= 0);
+    __builtin_assume(i_offset <  8);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI5_K; // == k if QK_K == 256
+
+    const block_q5_K * bx0 = (block_q5_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR5_K*kqsx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
+        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
+        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
+
+        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
+        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
+
+        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
+        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI5_K) {
+        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % GGML_CUDA_MMQ_Y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
+
+        const int * scales = (int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    __builtin_assume(i >= 0);
+    __builtin_assume(i <  GGML_CUDA_MMQ_Y);
+    __builtin_assume(j >= 0);
+    __builtin_assume(j <  WARP_SIZE);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+    __builtin_assume(k % VDR_Q5_K_Q8_1_MMQ == 0);
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
+
+    const int index_x = i * (QR5_K*WARP_SIZE + 1) + QR5_K*k;
+    const int index_y = j * (QR5_K*WARP_SIZE)     + QR5_K*k;
+    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8, x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
+}
+
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
 
-        const int vi1 = (v1 >> (4*i)) & 0x0F0F0F0F;
-        const int vi2 = (v2 >> (4*i)) & 0x0F0F0F0F;
+    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
+    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
+    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
 
-        const int dot1 = __dp4a(vi2, ui2, __dp4a(vi1, ui1, 0)); // SIMD dot product
-        const int dot2 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
+    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
+    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
 
-        sumf_d += d8i * (dot1 * sc[i]);
-        sumf_m += d8i * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
-    }
+    const int8_t * scales = bq6_K->scales + scale_offset;
 
-    return d*sumf_d - dmin*sumf_m;
+    int    u[QR6_K];
+    float d8[QR6_K];
 
-#else
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
+        d8[i] = bq8_1[bq8_offset + 2*i].ds.x;
+    }
 
-    uint16_t aux16[2];
-    const uint8_t * s = (const uint8_t *)aux16;
+    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
+}
 
-    const uint16_t * a = (const uint16_t *)bq4_K->scales;
-    aux16[0] = a[0] & 0x0f0f;
-    aux16[1] = (a[0] >> 4) & 0x0f0f;
+static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
 
-    const float dall = bq4_K->d[0];
-    const float dmin = bq4_K->d[1];
+    __shared__ int   tile_x_ql[GGML_CUDA_MMQ_Y * (2*WARP_SIZE)     + GGML_CUDA_MMQ_Y];
+    __shared__ half2 tile_x_dm[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI6_K) + GGML_CUDA_MMQ_Y/QI6_K];
+    __shared__ int   tile_x_sc[GGML_CUDA_MMQ_Y * (WARP_SIZE/8)     + GGML_CUDA_MMQ_Y/8];
 
-    const float d8_1 = bq8_1[0].d;
-    const float d8_2 = bq8_1[1].d;
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
 
-    const int ui1 = *((const int *)bq8_1[0].qs + iqs);
-    const int ui2 = *((const int *)bq8_1[0].qs + iqs + 4);
-    const int ui3 = *((const int *)bq8_1[1].qs + iqs);
-    const int ui4 = *((const int *)bq8_1[1].qs + iqs + 4);
+template <bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
 
-    const int * q4 = (const int *)bq4_K->qs + iqs;
-    const int v1 = q4[0];
-    const int v2 = q4[4];
+    __builtin_assume(i_offset >= 0);
+    __builtin_assume(i_offset <  8);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
 
-    const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0));
-    const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
-    const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
-    const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0));
+    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI6_K; // == k if QK_K == 256
 
-    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
-    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
+    const block_q6_K * bx0 = (block_q6_K *) vx;
 
-    return dall * sumf_d - dmin * sumf_m;
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
+        int i = i0 + i_offset;
 
-#endif
+        if (need_check) {
+            i = min(i, i_max);
+        }
 
-#else
-    return 0.0f; // only to satisfy the compiler
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR6_K*kqsx;
 
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
+        const int ql = get_int_from_uint8(bxi->ql, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
+        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
+        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
 
-#ifndef GGML_QKK_64
+        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
+        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
 
-    const int bq8_offset = QR5_K * (iqs / (QI8_1/2));
-    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * (iqs%4));
-    const int * qh = (const int *)(bq5_K->qh + 4 * (iqs%4));
+        x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
+        x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
+    }
 
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
+    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+    float * x_dmf = (float *) x_dm;
 
-    const float    d = bq5_K->d;
-    const float dmin = bq5_K->dmin;
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI6_K) {
+        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
 
-    const int vl1 = ql[0];
-    const int vl2 = ql[4];
+        if (need_check) {
+            i = min(i, i_max);
+        }
 
-    const int vh1 = qh[0] >> bq8_offset;
-    const int vh2 = qh[4] >> bq8_offset;
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
 
-    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
-    uint16_t aux[2];
-    const int j = bq8_offset/2;
-    if (j < 2) {
-        aux[0] = scales[j+0] & 0x3f3f;
-        aux[1] = scales[j+2] & 0x3f3f;
-    } else {
-        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
-        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
     }
-    const uint8_t * sc = (const uint8_t *)aux;
-    const uint8_t * m  = sc + 2;
 
-    for (int i = 0; i < QR5_K; ++i) {
+#pragma unroll
+    for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % GGML_CUDA_MMQ_Y;
 
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        const float d8i = bq8i->d;
-        const int * q8 = (const int *)bq8i->qs + (iqs%4);
-        const int ui1 = q8[0];
-        const int ui2 = q8[4];
+        if (need_check) {
+            i = min(i, i_max);
+        }
 
-        const int vil1 = (vl1 >> (4*i)) & 0x0F0F0F0F;
-        const int vil2 = (vl2 >> (4*i)) & 0x0F0F0F0F;
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
 
-        const int vih1 = ((vh1 >> i) << 4) & 0x10101010;
-        const int vih2 = ((vh2 >> i) << 4) & 0x10101010;
+        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
+    }
+}
 
-        const int vi1 = vil1 | vih1;
-        const int vi2 = vil2 | vih2;
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
 
-        const int dot1 = __dp4a(vi2, ui2, __dp4a(vi1, ui1, 0)); // SIMD dot product
-        const int dot2 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
+    __builtin_assume(i >= 0);
+    __builtin_assume(i <  GGML_CUDA_MMQ_Y);
+    __builtin_assume(j >= 0);
+    __builtin_assume(j <  WARP_SIZE);
+    __builtin_assume(k >= 0);
+    __builtin_assume(k <  WARP_SIZE);
+    __builtin_assume(k % VDR_Q6_K_Q8_1_MMQ == 0);
 
-        sumf_d += d8i * (dot1 * sc[i]);
-        sumf_m += d8i * (dot2 * m[i]);
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
 
-    }
+    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
 
-    return d*sumf_d - dmin*sumf_m;
+    const int index_x = i * (QR6_K*WARP_SIZE + 1) + QR6_K*k;
+    const int index_y = j * (QR6_K*WARP_SIZE)     + QR6_K*k;
+    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
+}
 
-#else
+template <int qk, int qr, int qi, bool need_sum, typename block_q_t,
+              allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
+static __global__ void mul_mat_q(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
 
-    const int8_t * s = bq5_K->scales;
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
 
-    const float d = bq5_K->d;
+    const int blocks_per_row_x = ncols_x / qk;
+    const int blocks_per_col_y = nrows_y / QK8_1;
+    const int blocks_per_warp = WARP_SIZE / qi;
 
-    const float d8_1 = bq8_1[0].d;
-    const float d8_2 = bq8_1[1].d;
+    const int & ncols_dst = ncols_y;
 
-    const int ui1 = *((const int *)bq8_1[0].qs + iqs);
-    const int ui2 = *((const int *)bq8_1[0].qs + iqs + 4);
-    const int ui3 = *((const int *)bq8_1[1].qs + iqs);
-    const int ui4 = *((const int *)bq8_1[1].qs + iqs + 4);
+    const int tid_x = threadIdx.x;
+    const int tid_y = threadIdx.y;
 
-    const int * ql = (const int *)bq5_K->qs + iqs;
-    const int vl1 = ql[0];
-    const int vl2 = ql[4];
+    const int row_dst_0 = blockIdx.x*GGML_CUDA_MMQ_Y;
+    const int & row_x_0 = row_dst_0;
+    const int row_dst = row_dst_0 + tid_x;
 
-    const int step = 4 * iqs; // 0, 4, 8, 12
-    const int im = step/8; // = 0 for iqs = 0, 1, = 1 for iqs = 2, 3
-    const int in = step%8; // 0, 4, 0, 4
-    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
+    const int col_dst_0 = blockIdx.y*WARP_SIZE;
+    const int & col_y_0 = col_dst_0;
 
-    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
-    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
-    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
-    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
+    int   * tile_x_ql = nullptr;
+    half2 * tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
 
-    const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1])
-                       + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]);
+    allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);
 
-    return d * sumf_d;
+    const int blocks_per_tile_y_col = qr*WARP_SIZE/QI8_1;
 
-#endif
+    __shared__ int    tile_y_qs[(WARP_SIZE) * (qr*WARP_SIZE)];
+    __shared__ half2  tile_y_ds[(WARP_SIZE) * blocks_per_tile_y_col];
 
-#else
-    return 0.0f; // only to satisfy the compiler
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
+    float sum[GGML_CUDA_MMQ_Y/WARP_SIZE][4] = {0.0f};
 
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
+    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
+        load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
+                   tid_y, nrows_x-row_x_0-1, tid_x, blocks_per_row_x);
 
-    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
-    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
-    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
+        for (int ir = 0; ir < qr; ++ir) {
+            const int kqs = ir*WARP_SIZE + tid_x;
+            const int kbxd = kqs / QI8_1;
 
-    float sumf = 0.0f;
+            for (int i = 0; i < WARP_SIZE; i += 8) {
+                const int col_y_eff = min(col_y_0 + tid_y + i, ncols_y-1); // to prevent out-of-bounds memory accesses
 
-    const float d = bq6_K->d;
+                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
 
-    int vl;
-    memcpy(&vl, &bq6_K->ql[sizeof(int) * iqs], sizeof(int));
+                tile_y_qs[(tid_y + i) * (qr*WARP_SIZE) + kqs] = get_int_from_int8_aligned(by0->qs, tid_x % QI8_1);
+            }
+        }
 
-    int vh;
-    memcpy(&vh, &bq6_K->qh[sizeof(int) * ((QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4))], sizeof(int));
+        for (int ids0 = 0; ids0 < WARP_SIZE; ids0 += 8 * (WARP_SIZE/blocks_per_tile_y_col)) {
+            const int ids = (ids0 + tid_y * (WARP_SIZE/blocks_per_tile_y_col) + tid_x / blocks_per_tile_y_col) % WARP_SIZE;
+            const int kby = tid_x % blocks_per_tile_y_col;
+            const int col_y_eff = min(col_y_0 + ids, ncols_y-1);
 
-    for (int i = 0; i < QR6_K; ++i) {
-        const int sc = bq6_K->scales[scale_offset + 4*i];
+            // if the sum is not needed it's faster to transform the scale to f32 ahead of time
+            const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kby].ds;
+            half2       * dsi_dst = &tile_y_ds[ids * (qr*WARP_SIZE/QI8_1) + kby];
+            if (need_sum) {
+                *dsi_dst = *dsi_src;
+            } else {
+                float * dfi_dst = (float *) dsi_dst;
+                *dfi_dst = (*dsi_src).x;
+            }
+        }
 
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + 2*i;
-        const int ui = *((int*) &bq8i->qs[sizeof(int) * (iqs % (QI8_1))]);
-        const float d8i = bq8i->d;
+        __syncthreads();
 
-        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
+#if __CUDA_ARCH__ >= 700 // Unrolling the loop is slower on Pascal
+#pragma unroll
+#endif // __CUDA_ARCH__ >= 700
+        for (int k = 0; k < WARP_SIZE; k += vdr) {
+#pragma unroll
+            for (int j = 0; j < WARP_SIZE; j += 8) {
+#pragma unroll
+                for (int i = 0; i < GGML_CUDA_MMQ_Y; i += WARP_SIZE) {
+                    sum[i/WARP_SIZE][j/8] += vec_dot(tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds,
+                                                     tid_x + i, tid_y + j, k);
+                }
+            }
+        }
 
-        const int vih = ((vh >> (vh_shift + 4*i)) << 4) & 0x30303030;
+        __syncthreads();
+    }
 
-        const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
 
-        sumf += d8i * (__dp4a(vi, ui, 0) * sc); // SIMD dot product
+    if (row_dst >= nrows_dst) {
+        return;
     }
 
-    return d*sumf;
-#else
-    return 0.0f; // only to satisfy the compiler
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+    for (int j = 0; j < WARP_SIZE; j += 8) {
+        const int col_dst = col_dst_0 + j + tid_y;
+
+        if (col_dst >= ncols_dst) {
+            return;
+        }
+
+        for (int i = 0; i < GGML_CUDA_MMQ_Y; i += WARP_SIZE) {
+            dst[col_dst*nrows_dst + row_dst + i] = sum[i/WARP_SIZE][j/8];
+        }
+    }
 }
 
-template <int qk, int qi, typename block_q_t, vec_dot_q_cuda_t vec_dot_q_cuda>
+template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
 static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) {
     const int row = blockIdx.y*blockDim.y + threadIdx.y;
 
@@ -1813,7 +3326,7 @@ static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void *
     }
 
     const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = WARP_SIZE / qi;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
 
 // partial sum for each thread
     float tmp = 0.0f;
@@ -1822,11 +3335,11 @@ static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void *
     const block_q8_1 * y = (const block_q8_1 *) vy;
 
     for (int i = 0; i < blocks_per_row; i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i + threadIdx.x / qi; // x block index
+        const int ibx = row*blocks_per_row + i + threadIdx.x / (qi/vdr); // x block index
 
-        const int iby = (i + threadIdx.x / qi) * qk/QK8_1; // y block index that aligns with ibx
+        const int iby = (i + threadIdx.x / (qi/vdr)) * (qk/QK8_1); // y block index that aligns with ibx
 
-        const int iqs  = threadIdx.x % qi; // x block quant index when casting the quants to int
+        const int iqs  = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int
 
         tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
     }
@@ -1859,11 +3372,11 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons
     const int y_offset = qr == 1 ? 1 : qk/2;
 
 // partial sum for each thread
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
     half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
 #else
     float tmp = 0.0f;
-#endif // GGML_CUDA_DMMV_F16
+#endif // GGML_CUDA_F16
 
     for (int i = 0; i < ncols; i += iter_stride) {
         const int col = i + vals_per_iter*tid;
@@ -1883,7 +3396,7 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons
 
             // matrix multiplication
             // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
             tmp += __hmul2(v, {
                 y[iybs + iqs + j/qr + 0],
                 y[iybs + iqs + j/qr + y_offset]
@@ -1891,7 +3404,7 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons
 #else
             tmp += v.x * y[iybs + iqs + j/qr + 0];
             tmp += v.y * y[iybs + iqs + j/qr + y_offset];
-#endif // GGML_CUDA_DMMV_F16
+#endif // GGML_CUDA_F16
         }
     }
 
@@ -1902,11 +3415,11 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons
     }
 
     if (tid == 0) {
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
         dst[row] = tmp.x + tmp.y;
 #else
         dst[row] = tmp;
-#endif // GGML_CUDA_DMMV_F16
+#endif // GGML_CUDA_F16
     }
 }
 
@@ -2046,7 +3559,8 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
 }
 
 // rope == RoPE == rotary positional embedding
-static __global__ void rope_f32(const float * x, float * dst, const int ncols, const float p, const float theta_scale) {
+static __global__ void rope_f32(const float * x, float * dst, const int ncols, const float p0,
+                                const float p_delta, const int p_delta_rows, const float theta_scale) {
     const int col = 2*(blockDim.x*blockIdx.x + threadIdx.x);
 
     if (col >= ncols) {
@@ -2056,7 +3570,7 @@ static __global__ void rope_f32(const float * x, float * dst, const int ncols, c
     const int row = blockDim.y*blockIdx.y + threadIdx.y;
     const int i = row*ncols + col;
 
-    const float theta = p*powf(theta_scale, col/2);
+    const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
     const float sin_theta = sinf(theta);
     const float cos_theta = cosf(theta);
 
@@ -2203,9 +3717,11 @@ static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, con
     rms_norm_f32<<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
 }
 
-static void quantize_row_q8_1_cuda(const float * x, void * vy, const int ndata, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
-    quantize_q8_1<<<num_blocks, CUDA_QUANTIZE_BLOCK_SIZE, 0, stream>>>(x, vy, ndata, k);
+static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) {
+    const int block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    const dim3 num_blocks(block_num_x, ky, 1);
+    const dim3 block_size(CUDA_DEQUANTIZE_BLOCK_SIZE, 1, 1);
+    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
 }
 
 static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
@@ -2366,7 +3882,7 @@ static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float *
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, vec_dot_q4_0_q8_1>
+    mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
@@ -2375,7 +3891,7 @@ static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float *
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, vec_dot_q4_1_q8_1>
+    mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
@@ -2384,7 +3900,7 @@ static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float *
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, vec_dot_q5_0_q8_1>
+    mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
@@ -2393,7 +3909,7 @@ static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float *
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, vec_dot_q5_1_q8_1>
+    mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
@@ -2402,7 +3918,7 @@ static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float *
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, vec_dot_q8_0_q8_1>
+    mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
@@ -2411,7 +3927,7 @@ static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float *
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI2_K, block_q2_K, vec_dot_q2_K_q8_1>
+    mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
@@ -2420,7 +3936,7 @@ static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float *
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI3_K, block_q3_K, vec_dot_q3_K_q8_1>
+    mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
@@ -2429,10 +3945,7 @@ static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float *
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    // Note: we use QI4_K/2 instead of QI4_K to make the dot product template require 4 groups of quants to be processed per
-    //       kernel call instead of 2. This results in a better perfmance because the cost of computing the k-quant scales
-    //       is better amortized.
-    mul_mat_vec_q<QK_K, QI4_K/2, block_q4_K, vec_dot_q4_K_q8_1>
+    mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
@@ -2441,10 +3954,7 @@ static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float *
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    // Note: we use QI5_K/2 instead of QI5_K to make the dot product template require 4 groups of quants to be processed per
-    //       kernel call instead of 2. This results in a better perfmance because the cost of computing the k-quant scales
-    //       is better amortized.
-    mul_mat_vec_q<QK_K, QI5_K/2, block_q5_K, vec_dot_q5_K_q8_1>
+    mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
@@ -2453,7 +3963,7 @@ static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float *
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI6_K, block_q6_K, vec_dot_q6_K_q8_1>
+    mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
@@ -2500,6 +4010,186 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
     }
 }
 
+static void ggml_mul_mat_q4_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    const int block_num_x = (nrows_x + GGML_CUDA_MMQ_Y - 1) / GGML_CUDA_MMQ_Y;
+    const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
+
+    if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
+        mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, allocate_tiles_q4_0, load_tiles_q4_0<false>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, allocate_tiles_q4_0, load_tiles_q4_0<true>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q4_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    const int block_num_x = (nrows_x + GGML_CUDA_MMQ_Y - 1) / GGML_CUDA_MMQ_Y;
+    const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
+
+    if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
+        mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, allocate_tiles_q4_1, load_tiles_q4_1<false>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, allocate_tiles_q4_1, load_tiles_q4_1<true>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    const int block_num_x = (nrows_x + GGML_CUDA_MMQ_Y - 1) / GGML_CUDA_MMQ_Y;
+    const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
+
+    if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
+        mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, allocate_tiles_q5_0, load_tiles_q5_0<false>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, allocate_tiles_q5_0, load_tiles_q5_0<true>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    const int block_num_x = (nrows_x + GGML_CUDA_MMQ_Y - 1) / GGML_CUDA_MMQ_Y;
+    const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
+
+    if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
+        mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, allocate_tiles_q5_1, load_tiles_q5_1<false>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, allocate_tiles_q5_1, load_tiles_q5_1<true>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q8_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    const int block_num_x = (nrows_x + GGML_CUDA_MMQ_Y - 1) / GGML_CUDA_MMQ_Y;
+    const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
+
+    if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
+        mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, allocate_tiles_q8_0, load_tiles_q8_0<false>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, allocate_tiles_q8_0, load_tiles_q8_0<true>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q2_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    const int block_num_x = (nrows_x + GGML_CUDA_MMQ_Y - 1) / GGML_CUDA_MMQ_Y;
+    const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
+
+    if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
+        mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, allocate_tiles_q2_K, load_tiles_q2_K<false>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, allocate_tiles_q2_K, load_tiles_q2_K<true>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q3_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    const int block_num_x = (nrows_x + GGML_CUDA_MMQ_Y - 1) / GGML_CUDA_MMQ_Y;
+    const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
+
+    if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
+        mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, allocate_tiles_q3_K, load_tiles_q3_K<false>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, allocate_tiles_q3_K, load_tiles_q3_K<true>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q4_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    const int block_num_x = (nrows_x + GGML_CUDA_MMQ_Y - 1) / GGML_CUDA_MMQ_Y;
+    const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
+
+    if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
+        mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, allocate_tiles_q4_K, load_tiles_q4_K<false>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, allocate_tiles_q4_K, load_tiles_q4_K<true>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    const int block_num_x = (nrows_x + GGML_CUDA_MMQ_Y - 1) / GGML_CUDA_MMQ_Y;
+    const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
+
+    if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
+        mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, allocate_tiles_q5_K, load_tiles_q5_K<false>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, allocate_tiles_q5_K, load_tiles_q5_K<true>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q6_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    const int block_num_x = (nrows_x + GGML_CUDA_MMQ_Y - 1) / GGML_CUDA_MMQ_Y;
+    const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
+
+    if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
+        mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, allocate_tiles_q6_K, load_tiles_q6_K<false>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, allocate_tiles_q6_K, load_tiles_q6_K<true>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+            <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
 static void ggml_mul_mat_p021_f16_f32_cuda(
     const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x,
     const int nchannels_x, const int nchannels_y, cudaStream_t stream) {
@@ -2544,12 +4234,13 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons
     scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
 }
 
-static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p, const float theta_scale, cudaStream_t stream) {
+static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
+                          const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
     GGML_ASSERT(nrows % 2 == 0);
     const dim3 block_dims(2*CUDA_ROPE_BLOCK_SIZE, 1, 1);
     const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(num_blocks_x, nrows, 1);
-    rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p, theta_scale);
+    rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
 }
 
 static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p, const float block_p, const float theta_scale, cudaStream_t stream) {
@@ -2597,6 +4288,7 @@ struct cuda_buffer {
 
 static cuda_buffer g_cuda_buffer_pool[GGML_CUDA_MAX_DEVICES][MAX_CUDA_BUFFERS];
 static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;
+static bool g_mul_mat_q = false;
 
 static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
     scoped_spin_lock lock(g_cuda_pool_lock);
@@ -2630,7 +4322,7 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
         b.size = 0;
         return ptr;
     }
-    if(worst_i!=-1) //no buffer that fits our needs, resize largest one to save memory
+    if(worst_i!=-1 && !g_mul_mat_q) //no buffer that fits our needs, resize largest one to save memory (non mmq only)
     {
         cuda_buffer& b = g_cuda_buffer_pool[id][worst_i];
         b.size = 0;
@@ -2639,8 +4331,12 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
         b.ptr = ptr = nullptr;
     }
     void * ptr;
-    CUDA_CHECK(cudaMalloc((void **) &ptr, size));
-    *actual_size = size;
+
+    size_t look_ahead_size = (size_t) (1.05 * size);
+    look_ahead_size = 256 * ((look_ahead_size + 255)/256);
+    CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size));
+    *actual_size = look_ahead_size;
+
     return ptr;
 }
 
@@ -2668,9 +4364,7 @@ static size_t g_scratch_offset = 0;
 
 static int g_device_count = -1;
 static int g_main_device = 0;
-#ifndef GGML_CUDA_FORCE_DMMV
 static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
-#endif
 static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
 
 static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
@@ -2693,9 +4387,7 @@ void ggml_init_cublas() {
             g_tensor_split[id] = total_vram;
             total_vram += prop.totalGlobalMem;
 
-#ifndef GGML_CUDA_FORCE_DMMV
             g_compute_capabilities[id] = 100*prop.major + 10*prop.minor;
-#endif
         }
         for (int id = 0; id < g_device_count; ++id) {
             g_tensor_split[id] /= total_vram;
@@ -2957,6 +4649,83 @@ inline void ggml_cuda_op_rms_norm(
     (void) i1;
 }
 
+inline void ggml_cuda_op_mul_mat_q(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
+    float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
+    cudaStream_t & cudaStream_main){
+
+    GGML_ASSERT(src0_ddq_i != nullptr);
+    GGML_ASSERT(src1_ddf_i != nullptr);
+    GGML_ASSERT(dst_ddf_i != nullptr);
+
+    const int64_t ne00 = src0->ne[0];
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne0 = dst->ne[0];
+
+    const int64_t i01_diff = i01_high - i01_low;
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
+    const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : i01_diff;
+
+    const int64_t padded_row_size = ne10 % MATRIX_ROW_PADDING == 0 ?
+        ne10 : ne10 - ne10 % MATRIX_ROW_PADDING + MATRIX_ROW_PADDING;
+    size_t as;
+    void * src1_q8_1 = ggml_cuda_pool_malloc(padded_row_size*ne11*sizeof(block_q8_1)/QK8_1, &as);
+    quantize_row_q8_1_cuda(src1_ddf_i, src1_q8_1, ne10, ne11, padded_row_size, cudaStream_main);
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            ggml_mul_mat_q4_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, i01_diff, ne11, padded_row_size, nrows_dst, cudaStream_main);
+            break;
+        case GGML_TYPE_Q4_1:
+            ggml_mul_mat_q4_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, i01_diff, ne11, padded_row_size, nrows_dst, cudaStream_main);
+            break;
+        case GGML_TYPE_Q5_0:
+            ggml_mul_mat_q5_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, i01_diff, ne11, padded_row_size, nrows_dst, cudaStream_main);
+            break;
+        case GGML_TYPE_Q5_1:
+            ggml_mul_mat_q5_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, i01_diff, ne11, padded_row_size, nrows_dst, cudaStream_main);
+            break;
+        case GGML_TYPE_Q8_0:
+            ggml_mul_mat_q8_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, i01_diff, ne11, padded_row_size, nrows_dst, cudaStream_main);
+            break;
+        case GGML_TYPE_Q2_K:
+            ggml_mul_mat_q2_K_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, i01_diff, ne11, padded_row_size, nrows_dst, cudaStream_main);
+            break;
+        case GGML_TYPE_Q3_K:
+            ggml_mul_mat_q3_K_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, i01_diff, ne11, padded_row_size, nrows_dst, cudaStream_main);
+            break;
+        case GGML_TYPE_Q4_K:
+            ggml_mul_mat_q4_K_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, i01_diff, ne11, padded_row_size, nrows_dst, cudaStream_main);
+            break;
+        case GGML_TYPE_Q5_K:
+            ggml_mul_mat_q5_K_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, i01_diff, ne11, padded_row_size, nrows_dst, cudaStream_main);
+            break;
+        case GGML_TYPE_Q6_K:
+            ggml_mul_mat_q6_K_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, i01_diff, ne11, padded_row_size, nrows_dst, cudaStream_main);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    ggml_cuda_pool_free(src1_q8_1, as);
+
+    (void) src1;
+    (void) dst;
+    (void) src0_ddf_i;
+    (void) i02;
+    (void) i1;
+}
+
 inline void ggml_cuda_op_mul_mat_vec(
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
     float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
@@ -2971,6 +4740,7 @@ inline void ggml_cuda_op_mul_mat_vec(
 
 #ifdef GGML_CUDA_FORCE_DMMV
     const bool use_mul_mat_vec_q = false;
+    (void) g_compute_capabilities[0];
 #else
     int id;
     CUDA_CHECK(cudaGetDevice(&id));
@@ -2998,7 +4768,7 @@ inline void ggml_cuda_op_mul_mat_vec(
             ne00 : ne00 - ne00 % MATRIX_ROW_PADDING + MATRIX_ROW_PADDING;
         size_t as;
         void * src1_q8_1 = ggml_cuda_pool_malloc(padded_row_size*sizeof(block_q8_1)/QK8_1, &as);
-        quantize_row_q8_1_cuda(src1_ddf_i, src1_q8_1, ne00, padded_row_size, cudaStream_main);
+        quantize_row_q8_1_cuda(src1_ddf_i, src1_q8_1, ne00, 1, padded_row_size, cudaStream_main);
 
         switch (src0->type) {
             case GGML_TYPE_Q4_0:
@@ -3039,7 +4809,7 @@ inline void ggml_cuda_op_mul_mat_vec(
         ggml_cuda_pool_free(src1_q8_1, as);
     } else {
         // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
         size_t ash;
         dfloat * src1_dfloat = nullptr; // dfloat == half
 
@@ -3055,7 +4825,7 @@ inline void ggml_cuda_op_mul_mat_vec(
         }
 #else
         dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion
-#endif // GGML_CUDA_DMMV_F16
+#endif // GGML_CUDA_F16
 
         switch (src0->type) {
             case GGML_TYPE_Q4_0:
@@ -3096,11 +4866,11 @@ inline void ggml_cuda_op_mul_mat_vec(
                 break;
         }
 
-#ifdef GGML_CUDA_DMMV_F16
+#ifdef GGML_CUDA_F16
         if (src1_convert_f16) {
             ggml_cuda_pool_free(src1_dfloat, ash);
         }
-#endif // GGML_CUDA_DMMV_F16
+#endif // GGML_CUDA_F16
     }
 
     (void) src1;
@@ -3160,6 +4930,7 @@ inline void ggml_cuda_op_rope(
     GGML_ASSERT(dst_ddf_i != nullptr);
 
     const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
     const int64_t i01_diff = i01_high - i01_low;
 
     const int n_past = ((int32_t *) dst->op_params)[0];
@@ -3173,17 +4944,18 @@ inline void ggml_cuda_op_rope(
     memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
 
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float p = (((mode & 1) == 0 ? n_past + i02 : i02)) * freq_scale;
 
-    bool is_glm = mode & 4;
+    const bool is_glm = mode & 4;
 
     // compute
     if (is_glm) {
+        const float p = (((mode & 1) == 0 ? n_past + i02 : i02)) * freq_scale;
         const float id_p = min(p, n_ctx - 2.f);
         const float block_p = max(p - (n_ctx - 2.f), 0.f);
         rope_glm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, id_p, block_p, theta_scale, cudaStream_main);
     } else {
-        rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p, theta_scale, cudaStream_main);
+        const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
+        rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p0, freq_scale, ne01, theta_scale, cudaStream_main);
     }
 
     (void) src1;
@@ -3355,7 +5127,14 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
         int64_t row_low, row_high;
         if (split) {
             row_low = id == 0 ? 0 : nrows0*g_tensor_split[id];
-            row_high = id == g_device_count - 1 ? nrows0 : nrows0*g_tensor_split[id + 1];
+            row_low -= row_low % GGML_CUDA_MMQ_Y;
+
+            if (id == g_device_count - 1) {
+                row_high = nrows0;
+            } else {
+                row_high = nrows0*g_tensor_split[id + 1];
+                row_high -= row_high % GGML_CUDA_MMQ_Y;
+            }
         } else {
             row_low = 0;
             row_high = nrows0*i02_divisor;
@@ -3521,13 +5300,12 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
                     if (split) {
                         // src0 = weight matrix is saved as a transposed matrix for better memory layout.
                         // dst is NOT transposed.
-                        // The outputs of cuBLAS matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU.
+                        // The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU.
                         // Instead they need to be copied to the correct slice in ne0 = dst row index.
                         // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results.
-                        for (int64_t j = 0; j < ne1; ++j) {
-                            float * dhf_dst_i = (float *) ((char *) dst_off_device + (j*ne0 + i01_low)*sizeof(float) + i02*nb2 + i03*nb3);
-                            CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_ddf_i + j*i01_diff, i01_diff*sizeof(float), kind, cudaStream_main));
-                        }
+                        float * dhf_dst_i = (float *) ((char *) dst_off_device + i01_low*sizeof(float) + i02*nb2 + i03*nb3);
+                        CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float), dst_ddf_i, i01_diff*sizeof(float),
+                                                     i01_diff*sizeof(float), ne1, kind, cudaStream_main));
                     } else {
                         float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
                         CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_ddf_i, dst_stride*sizeof(float), kind, cudaStream_main));
@@ -3568,7 +5346,7 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
     if (split && g_device_count > 1) {
         CUDA_CHECK(cudaSetDevice(g_main_device));
         for (int id = 0; id < g_device_count; ++id) {
-            if (id != g_main_device) {
+            if (id != g_main_device && src0_extra->events[id]) {
                 CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams_main[g_main_device], src0_extra->events[id]));
             }
         }
@@ -3710,7 +5488,19 @@ void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_
         if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) {
             ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_vec, false, false);
         } else {
-            ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false);
+            int min_compute_capability = INT_MAX;
+            for (int id = 0; id < g_device_count; ++id) {
+                if (min_compute_capability > g_compute_capabilities[id]
+                        && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
+                    min_compute_capability = g_compute_capabilities[id];
+                }
+            }
+
+            if (g_mul_mat_q && ggml_is_quantized(src0->type) && min_compute_capability >= MIN_CC_DP4A) {
+                ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_q, false, false);
+            } else {
+                ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false);
+            }
         }
     } else {
         GGML_ASSERT(false);
@@ -3787,7 +5577,10 @@ void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml
 
 void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
-    ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, false); // FIXME flatten changes results
+
+    const int mode = ((int32_t *) dst->op_params)[2];
+    const bool is_glm = mode & 4;
+    ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, !is_glm); // flatten support not implemented for glm
 }
 
 void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -3820,7 +5613,14 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
             row_high = nrows;
         } else if (backend == GGML_BACKEND_GPU_SPLIT) {
             row_low = id == 0 ? 0 : nrows*g_tensor_split[id];
-            row_high = id == g_device_count - 1 ? nrows : nrows*g_tensor_split[id + 1];
+            row_low -= row_low % GGML_CUDA_MMQ_Y;
+
+            if (id == g_device_count - 1) {
+                row_high = nrows;
+            } else {
+                row_high = nrows*g_tensor_split[id + 1];
+                row_high -= row_high % GGML_CUDA_MMQ_Y;
+            }
         } else {
             GGML_ASSERT(false);
         }
@@ -3994,6 +5794,10 @@ void ggml_cuda_set_main_device(int main_device) {
     }
 }
 
+void ggml_cuda_set_mul_mat_q(bool mul_mat_q) {
+    g_mul_mat_q = mul_mat_q;
+}
+
 void ggml_cuda_set_scratch_size(size_t scratch_size) {
     g_scratch_size = scratch_size;
 }
diff --git a/ggml-cuda.h b/ggml-cuda.h
index 3c1e8deb6a6ddbfbb43e22bd01cc64e560fe313d..72d7afa463d741498af0063f0ccf14e5b9028bf9 100644
--- a/ggml-cuda.h
+++ b/ggml-cuda.h
@@ -27,6 +27,7 @@ void   ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
 void   ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
 void   ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
 void   ggml_cuda_set_main_device(int main_device);
+void   ggml_cuda_set_mul_mat_q(bool mul_mat_q);
 void   ggml_cuda_set_scratch_size(size_t scratch_size);
 void   ggml_cuda_free_scratch(void);
 bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
diff --git a/ggml-metal.m b/ggml-metal.m
index 74a6bff40411784f2b13ca4c1a7bf607bfc400c4..b47a98e214b613fb0f022f8606047e6771201ddd 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -7,6 +7,11 @@
 #import <Metal/Metal.h>
 #import <MetalPerformanceShaders/MetalPerformanceShaders.h>
 
+#undef MIN
+#undef MAX
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
 #ifdef GGML_METAL_NDEBUG
 #define metal_printf(...)
 #else
@@ -15,6 +20,8 @@
 
 #define UNUSED(x) (void)(x)
 
+#define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
+
 struct ggml_metal_buffer {
     const char * name;
 
@@ -36,7 +43,7 @@ struct ggml_metal_context {
     int n_buffers;
     struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
 
-    int concur_list[GGML_MAX_NODES];
+    int concur_list[GGML_MAX_CONCUR];
     int concur_list_len;
 
     // custom kernels
@@ -370,15 +377,15 @@ void ggml_metal_graph_find_concurrency(
         struct ggml_metal_context * ctx,
         struct ggml_cgraph * gf) {
     int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time
-    int nodes_unused[GGML_MAX_NODES];
+    int nodes_unused[GGML_MAX_CONCUR];
 
-    for (int i = 0; i < GGML_MAX_NODES; i++) {ctx->concur_list[i] = 0;}
-    for (int i = 0; i < gf->n_nodes; i++) {nodes_unused[i] = 1;}
+    for (int i = 0; i < GGML_MAX_CONCUR; i++) { ctx->concur_list[i] = 0; }
+    for (int i = 0; i < gf->n_nodes;     i++) { nodes_unused[i]     = 1; }
     ctx->concur_list_len = 0;
 
-    int n_left = gf->n_nodes;
-    int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
-    int level_pos = 0;  // at ctx->concur_list, the last layer (level) ends at level_pos
+    int n_left    = gf->n_nodes;
+    int n_start   = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
+    int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos
 
     while (n_left > 0) {
         // number of nodes at a layer (that can be issued concurrently)
@@ -386,28 +393,40 @@ void ggml_metal_graph_find_concurrency(
         for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) {
             if (nodes_unused[i]) {
                 // if the requirements for gf->nodes[i] are satisfied
-                int exe_flag=1;
+                int exe_flag = 1;
+
                 // scan all srcs
                 for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) {
                     struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind];
                     if (src_cur) {
                         // if is leaf nodes it's satisfied.
-                        if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {continue;}
+                        // TODO: ggml_is_leaf()
+                        if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {
+                            continue;
+                        }
 
                         // otherwise this src should be the output from previous nodes.
                         int is_found = 0;
+
                         // scan 2*search_depth back because we inserted barrier.
-                        for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
-                            if (gf->nodes[ctx->concur_list[j]] == src_cur) {is_found = 1; break;}
+                        //for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
+                        for (int j = MAX(0, level_pos - 2*search_depth); j < level_pos; j++) {
+                            if (ctx->concur_list[j] >= 0 && gf->nodes[ctx->concur_list[j]] == src_cur) {
+                                is_found = 1;
+                                break;
+                            }
+                        }
+                        if (is_found == 0) {
+                            exe_flag = 0;
+                            break;
                         }
-                        if (is_found == 0) {exe_flag = 0; break;}
                     }
                 }
                 if (exe_flag) {
                     // check if nodes[i]'s data will be overwritten by a node before nodes[i].
                     // if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3]
                     int64_t data_start = (int64_t) gf->nodes[i]->data;
-                    int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]);
+                    int64_t length     = (int64_t) ggml_nbytes(gf->nodes[i]);
                     for (int j = n_start; j < i; j++) {
                         if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \
                                             && gf->nodes[j]->op != GGML_OP_VIEW \
@@ -416,9 +435,9 @@ void ggml_metal_graph_find_concurrency(
                             if (((int64_t)gf->nodes[j]->data) >= data_start + length || \
                                 ((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) {
                                 continue;
-                            } else {
-                                exe_flag = 0;
                             }
+
+                            exe_flag = 0;
                         }
                     }
                 }
@@ -435,11 +454,13 @@ void ggml_metal_graph_find_concurrency(
         ctx->concur_list[level_pos + concurrency] = -1;
         ctx->concur_list_len++;
         // jump all sorted nodes at nodes_bak
-        while (!nodes_unused[n_start]) {n_start++;}
+        while (!nodes_unused[n_start]) {
+            n_start++;
+        }
         level_pos += concurrency + 1;
     }
 
-    if (ctx->concur_list_len > GGML_MAX_NODES) {
+    if (ctx->concur_list_len > GGML_MAX_CONCUR) {
         fprintf(stderr, "%s: too many elements for metal ctx->concur_list!\n", __func__);
     }
 }
@@ -453,7 +474,7 @@ void ggml_metal_graph_compute(
     // else fallback to serial dispatch
     MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor;
 
-    const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_NODES;
+    const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_CONCUR;
 
     const int n_nodes  = has_concur ? ctx->concur_list_len      : gf->n_nodes;
     edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial;
@@ -718,7 +739,8 @@ void ggml_metal_graph_compute(
                             // TODO: needs to be updated after PR: https://github.com/ggerganov/ggml/pull/224
 
                             GGML_ASSERT(ne00 == ne10);
-                            GGML_ASSERT(ne02 == ne12);
+                            // GGML_ASSERT(ne02 == ne12); // Should be checked on individual data types until broadcast is implemented everywhere
+                            GGML_ASSERT(ne03 == ne13);
 
                             if (ggml_is_contiguous(src0) &&
                                 ggml_is_contiguous(src1) &&
@@ -746,11 +768,11 @@ void ggml_metal_graph_compute(
                                     initWithDevice:ctx->device transposeLeft:false transposeRight:true
                                         resultRows:ne11 resultColumns:ne01 interiorColumns:ne00 alpha:1.0 beta:0.0];
 
-                                // we need to do ne02 multiplications
+                                // we need to do ne12 multiplications
                                 // TODO: is there a way to do this in parallel - currently very slow ..
                                 // TODO: might be possible to offload part of the computation to ANE using Accelerate's CBLAS
-                                for (int64_t i02 = 0; i02 < ne02; ++i02) {
-                                    size_t offs_src0_cur = offs_src0 + i02*nb02;
+                                for (int64_t i02 = 0; i02 < ne12; ++i02) {
+                                    size_t offs_src0_cur = offs_src0 + i02/(ne12/ne02)*nb02; // gqa not used for now
                                     size_t offs_src1_cur = offs_src1 + i02*nb12;
                                     size_t offs_dst_cur  = offs_dst  + i02*nb2;
 
@@ -772,8 +794,6 @@ void ggml_metal_graph_compute(
                                 switch (src0t) {
                                     case GGML_TYPE_F16:
                                         {
-                                            GGML_ASSERT(ne02 == ne12);
-
                                             nth0 = 64;
                                             nth1 = 1;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
@@ -853,16 +873,18 @@ void ggml_metal_graph_compute(
                                 [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
                                 [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
                                 [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
-                                [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:5];
-                                [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:6];
-                                [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:7];
-                                [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:8];
-                                [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:9];
-                                [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10];
-                                [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11];
-                                [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12];
-                                [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:13];
-                                [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:14];
+                                [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
+                                [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
+                                [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
+                                [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
+                                [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9];
+                                [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10];
+                                [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11];
+                                [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12];
+                                [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13];
+                                [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
+                                [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:15];
+                                [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:16];
 
                                 if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
                                     src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) {
diff --git a/ggml-metal.metal b/ggml-metal.metal
index 696b33ce75cf4fa8d92850ef5e762c32190d6410..8d26b5ec2dfa4f649b7a6d478f73238a0c700da6 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -509,11 +509,13 @@ kernel void kernel_mul_mat_f16_f32(
         device       float * dst,
         constant   int64_t & ne00,
         constant   int64_t & ne01,
+        constant   int64_t & ne02,
         constant  uint64_t & nb00,
         constant  uint64_t & nb01,
         constant  uint64_t & nb02,
         constant   int64_t & ne10,
         constant   int64_t & ne11,
+        constant   int64_t & ne12,
         constant  uint64_t & nb10,
         constant  uint64_t & nb11,
         constant  uint64_t & nb12,
@@ -529,7 +531,7 @@ kernel void kernel_mul_mat_f16_f32(
     const int64_t r1 = tgpig.y;
     const int64_t im = tgpig.z;
 
-    device const half  * x = (device const half  *) (src0 + r0*nb01 + im*nb02);
+    device const half  * x = (device const half  *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02);
     device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
 
     sum[tpitg.x] = 0.0f;
@@ -552,6 +554,7 @@ kernel void kernel_mul_mat_f16_f32(
     }
 }
 
+
 kernel void kernel_alibi_f32(
         device const float * src0,
         device       float * dst,
diff --git a/ggml.c b/ggml.c
index 1a22c9410979267fcf2a6189ace451229283d9b2..3c2792f8d0feff3ff4c37777c63c56b3b41129ed 100644
--- a/ggml.c
+++ b/ggml.c
@@ -195,8 +195,8 @@ typedef void * thread_ret_t;
 #define GGML_ALIGNED_MALLOC(size)  _aligned_malloc(size, GGML_MEM_ALIGN)
 #define GGML_ALIGNED_FREE(ptr)     _aligned_free(ptr)
 #else
-inline static void* ggml_aligned_malloc(size_t size) {
-    void* aligned_memory = NULL;
+inline static void * ggml_aligned_malloc(size_t size) {
+    void * aligned_memory = NULL;
 #ifdef GGML_USE_METAL
     int result = posix_memalign(&aligned_memory, getpagesize(), size);
 #else
@@ -3812,7 +3812,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "CROSS_ENTROPY_LOSS_BACK",
 };
 
-static_assert(GGML_OP_COUNT == 59, "GGML_OP_COUNT != 59");
+static_assert(GGML_OP_COUNT == 62, "GGML_OP_COUNT != 62");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -3884,7 +3884,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "cross_entropy_loss_back(x,y)",
 };
 
-static_assert(GGML_OP_COUNT == 59, "GGML_OP_COUNT != 59");
+static_assert(GGML_OP_COUNT == 62, "GGML_OP_COUNT != 62");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -4072,8 +4072,8 @@ bool ggml_is_numa(void) {
 ////////////////////////////////////////////////////////////////////////////////
 
 void ggml_print_object(const struct ggml_object * obj) {
-    GGML_PRINT(" - ggml_object: offset = %zu, size = %zu, next = %p\n",
-            obj->offs, obj->size, (const void *) obj->next);
+    GGML_PRINT(" - ggml_object: type = %d, offset = %zu, size = %zu, next = %p\n",
+            obj->type, obj->offs, obj->size, (const void *) obj->next);
 }
 
 void ggml_print_objects(const struct ggml_context * ctx) {
@@ -4111,7 +4111,7 @@ size_t ggml_nbytes(const struct ggml_tensor * tensor) {
     //
     // is enough, but just in case, adding the second part
 
-    return MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]);
+    return GGML_PAD(MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]), GGML_MEM_ALIGN);
 }
 
 size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
@@ -4213,7 +4213,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
 }
 
 size_t ggml_tensor_overhead(void) {
-    return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE + 16;
+    return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE;
 }
 
 bool ggml_is_transposed(const struct ggml_tensor * tensor) {
@@ -4254,7 +4254,7 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
 
-static inline bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
+bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
@@ -4384,7 +4384,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
         return NULL;
     }
 
-    const size_t mem_size = (params.mem_size + GGML_MEM_ALIGN - 1) & ~(GGML_MEM_ALIGN - 1);
+    const size_t mem_size = params.mem_buffer ? params.mem_size : GGML_PAD(params.mem_size, GGML_MEM_ALIGN);
 
     *ctx = (struct ggml_context) {
         /*.mem_size           =*/ mem_size,
@@ -4473,12 +4473,14 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
     struct ggml_object * obj = ctx->objects_begin;
 
     while (obj != NULL) {
-        struct ggml_tensor * tensor = (struct ggml_tensor *) ((char *) ctx->mem_buffer + obj->offs);
+        if (obj->type == GGML_OBJECT_TENSOR) {
+            struct ggml_tensor * tensor = (struct ggml_tensor *) ((char *) ctx->mem_buffer + obj->offs);
 
-        const size_t size = ggml_nbytes(tensor);
+            const size_t size = ggml_nbytes(tensor);
 
-        if (max_size < size) {
-            max_size = size;
+            if (max_size < size) {
+                max_size = size;
+            }
         }
 
         obj = obj->next;
@@ -4510,12 +4512,7 @@ static void ggml_scratch_load(struct ggml_context * ctx) {
 
 ////////////////////////////////////////////////////////////////////////////////
 
-static struct ggml_tensor * ggml_new_tensor_impl(
-        struct ggml_context * ctx,
-        enum   ggml_type type,
-        int    n_dims,
-        const int64_t* ne,
-        void*  data) {
+static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml_object_type type, size_t size) {
     // always insert objects at the end of the context's memory pool
     struct ggml_object * obj_cur = ctx->objects_end;
 
@@ -4523,77 +4520,81 @@ static struct ggml_tensor * ggml_new_tensor_impl(
     const size_t cur_size = obj_cur == NULL ? 0 : obj_cur->size;
     const size_t cur_end  = cur_offs + cur_size;
 
-    size_t size_needed = 0;
-
-    if (data == NULL && !ctx->no_alloc) {
-        size_needed += GGML_TYPE_SIZE[type]*(ne[0]/GGML_BLCK_SIZE[type]);
-        for (int i = 1; i < n_dims; i++) {
-            size_needed *= ne[i];
-        }
-        // align to GGML_MEM_ALIGN
-        size_needed = ((size_needed + GGML_MEM_ALIGN - 1)/GGML_MEM_ALIGN)*GGML_MEM_ALIGN;
-    }
+    // align to GGML_MEM_ALIGN
+    size_t size_needed = GGML_PAD(size, GGML_MEM_ALIGN);
 
     char * const mem_buffer = ctx->mem_buffer;
     struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end);
 
-    if (ctx->scratch.data == NULL || data != NULL) {
-        size_needed += GGML_TENSOR_SIZE;
+    if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
+        GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
+                __func__, cur_end + size_needed, ctx->mem_size);
+        assert(false);
+        return NULL;
+    }
 
-        if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
-            GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
-                    __func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size);
-            assert(false);
-            return NULL;
-        }
+    *obj_new = (struct ggml_object) {
+        .offs = cur_end + GGML_OBJECT_SIZE,
+        .size = size_needed,
+        .next = NULL,
+        .type = type,
+    };
 
-        *obj_new = (struct ggml_object) {
-            .offs = cur_end + GGML_OBJECT_SIZE,
-            .size = size_needed,
-            .next = NULL,
-        };
+    ggml_assert_aligned(mem_buffer + obj_new->offs);
+
+    if (obj_cur != NULL) {
+        obj_cur->next = obj_new;
     } else {
-        if (ctx->scratch.offs + size_needed > ctx->scratch.size) {
-            GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n",
-                    __func__, ctx->scratch.offs + size_needed, ctx->scratch.size);
-            assert(false);
-            return NULL;
+        // this is the first object in this context
+        ctx->objects_begin = obj_new;
+    }
+
+    ctx->objects_end = obj_new;
+
+    //printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size);
+
+    return obj_new;
+}
+
+static struct ggml_tensor * ggml_new_tensor_impl(
+        struct ggml_context * ctx,
+        enum   ggml_type      type,
+        int                   n_dims,
+        const int64_t       * ne,
+        void                * data) {
+
+    assert(n_dims >= 1 && n_dims <= GGML_MAX_DIMS);
+
+    size_t data_size = 0;
+
+    if (data == NULL && !ctx->no_alloc) {
+        data_size += GGML_TYPE_SIZE[type]*(ne[0]/GGML_BLCK_SIZE[type]);
+        for (int i = 1; i < n_dims; i++) {
+            data_size *= ne[i];
         }
+    }
 
-        if (cur_end + GGML_TENSOR_SIZE + GGML_OBJECT_SIZE > ctx->mem_size) {
-            GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
-                    __func__, cur_end + GGML_TENSOR_SIZE + GGML_OBJECT_SIZE, ctx->mem_size);
+    if (ctx->scratch.data != NULL && data == NULL) {
+        // allocate tensor data in the scratch buffer
+        if (ctx->scratch.offs + data_size > ctx->scratch.size) {
+            GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n",
+                    __func__, ctx->scratch.offs + data_size, ctx->scratch.size);
             assert(false);
             return NULL;
         }
 
         data = (char * const) ctx->scratch.data + ctx->scratch.offs;
 
-        *obj_new = (struct ggml_object) {
-            .offs = cur_end + GGML_OBJECT_SIZE,
-            .size = GGML_TENSOR_SIZE,
-            .next = NULL,
-        };
-
-        //printf("scratch offs = %zu, size_needed = %zu\n", ctx->scratch.offs, size_needed);
+        ctx->scratch.offs += data_size;
 
-        ctx->scratch.offs += size_needed;
+        data_size = 0;
     }
 
-    if (obj_cur != NULL) {
-        obj_cur->next = obj_new;
-    } else {
-        // this is the first object in this context
-        ctx->objects_begin = obj_new;
-    }
-
-    ctx->objects_end = obj_new;
+    struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TENSOR, GGML_TENSOR_SIZE + data_size);
 
-    //printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size);
-
-    struct ggml_tensor * const result = (struct ggml_tensor *)(mem_buffer + obj_new->offs);
+    // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here
 
-    ggml_assert_aligned(result);
+    struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs);
 
     *result = (struct ggml_tensor) {
         /*.type         =*/ type,
@@ -4602,7 +4603,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
         /*.ne           =*/ { 1, 1, 1, 1 },
         /*.nb           =*/ { 0, 0, 0, 0 },
         /*.op           =*/ GGML_OP_NONE,
-        /*.op_params    =*/ {0},
+        /*.op_params    =*/ { 0 },
         /*.is_param     =*/ false,
         /*.grad         =*/ NULL,
         /*.src          =*/ { NULL },
@@ -4634,6 +4635,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
 }
 
 static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) {
+    GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings
     assert(params_size <= GGML_MAX_OP_PARAMS);
     memcpy(tensor->op_params, params, params_size);
 }
@@ -4650,22 +4652,22 @@ static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int3
 
 struct ggml_tensor * ggml_new_tensor(
         struct ggml_context * ctx,
-        enum   ggml_type type,
-        int    n_dims,
-        const int64_t * ne) {
+        enum   ggml_type      type,
+        int                   n_dims,
+        const int64_t       * ne) {
     return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL);
 }
 
 struct ggml_tensor * ggml_new_tensor_1d(
         struct ggml_context * ctx,
-        enum   ggml_type type,
+        enum   ggml_type      type,
         int64_t ne0) {
     return ggml_new_tensor(ctx, type, 1, &ne0);
 }
 
 struct ggml_tensor * ggml_new_tensor_2d(
         struct ggml_context * ctx,
-        enum   ggml_type type,
+        enum   ggml_type      type,
         int64_t ne0,
         int64_t ne1) {
     const int64_t ne[2] = { ne0, ne1 };
@@ -4674,7 +4676,7 @@ struct ggml_tensor * ggml_new_tensor_2d(
 
 struct ggml_tensor * ggml_new_tensor_3d(
         struct ggml_context * ctx,
-        enum   ggml_type type,
+        enum   ggml_type      type,
         int64_t ne0,
         int64_t ne1,
         int64_t ne2) {
@@ -4984,11 +4986,6 @@ enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
     return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0);
 }
 
-static void ggml_set_unary_op(struct ggml_tensor * tensor, enum ggml_unary_op op) {
-    GGML_ASSERT(tensor->op = GGML_OP_UNARY);
-    ggml_set_op_params_i32(tensor, 0, (int32_t) op);
-}
-
 const char * ggml_get_name(const struct ggml_tensor * tensor) {
     return tensor->name;
 }
@@ -5027,9 +5024,11 @@ struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * nam
     char * const mem_buffer = ctx->mem_buffer;
 
     while (obj != NULL) {
-        struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs);
-        if (strcmp(cur->name, name) == 0) {
-            return cur;
+        if (obj->type == GGML_OBJECT_TENSOR) {
+            struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs);
+            if (strcmp(cur->name, name) == 0) {
+                return cur;
+            }
         }
 
         obj = obj->next;
@@ -6243,6 +6242,27 @@ struct ggml_tensor * ggml_reshape_4d(
 
 // ggml_view_1d
 
+static struct ggml_tensor * ggml_view_tensor_offset(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_dims,
+        const int64_t       * ne,
+        size_t                offset) {
+    // don't calculate an offset from an unallocated tensor
+    void * data = NULL;
+    if (a->data != NULL) {
+        data = (char *) a->data + offset;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, data);
+
+    ggml_format_name(result, "%s (view)", a->name);
+
+    ggml_set_op_params(result, &offset, sizeof(offset));
+
+    return result;
+}
+
 struct ggml_tensor * ggml_view_1d(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
@@ -6255,10 +6275,7 @@ struct ggml_tensor * ggml_view_1d(
         is_node = true;
     }
 
-    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset);
-    ggml_format_name(result, "%s (view)", a->name);
-
-    ggml_set_op_params(result, &offset, sizeof(offset));
+    struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 1, &ne0, offset);
 
     result->op   = GGML_OP_VIEW;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6285,10 +6302,7 @@ struct ggml_tensor * ggml_view_2d(
 
     const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, 1, 1 };
 
-    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset);
-    ggml_format_name(result, "%s (view)", a->name);
-
-    ggml_set_op_params(result, &offset, sizeof(offset));
+    struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 2, ne, offset);
 
     result->nb[1] = nb1;
     result->nb[2] = result->nb[1]*ne1;
@@ -6321,10 +6335,7 @@ struct ggml_tensor * ggml_view_3d(
 
     const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, 1 };
 
-    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, (char *) a->data + offset);
-    ggml_format_name(result, "%s (view)", a->name);
-
-    ggml_set_op_params(result, &offset, sizeof(offset));
+    struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 3, ne, offset);
 
     result->nb[1] = nb1;
     result->nb[2] = nb2;
@@ -6359,10 +6370,7 @@ struct ggml_tensor * ggml_view_4d(
 
     const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, ne3 };
 
-    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, (char *) a->data + offset);
-    ggml_format_name(result, "%s (view)", a->name);
-
-    ggml_set_op_params(result, &offset, sizeof(offset));
+    struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 4, ne, offset);
 
     result->nb[1] = nb1;
     result->nb[2] = nb2;
@@ -6433,7 +6441,7 @@ struct ggml_tensor * ggml_permute(
     result->src[0] = a;
 
     int32_t params[] = { axis0, axis1, axis2, axis3 };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     return result;
 }
@@ -6559,7 +6567,7 @@ static struct ggml_tensor * ggml_diag_mask_inf_impl(
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
     int32_t params[] = { n_past, inplace ? 1 : 0 };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_DIAG_MASK_INF;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6599,7 +6607,7 @@ static struct ggml_tensor * ggml_diag_mask_zero_impl(
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
     int32_t params[] = { n_past, inplace ? 1 : 0 };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_DIAG_MASK_ZERO;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6715,9 +6723,9 @@ static struct ggml_tensor * ggml_rope_impl(
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
     int32_t params[6] = { n_past, n_dims, mode, n_ctx };
-    memcpy(params + 4, &freq_base, sizeof(float));
+    memcpy(params + 4, &freq_base,  sizeof(float));
     memcpy(params + 5, &freq_scale, sizeof(float));
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_ROPE;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6746,6 +6754,18 @@ struct ggml_tensor * ggml_rope_inplace(
     return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, true);
 }
 
+struct ggml_tensor * ggml_rope_custom(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_past,
+        int                   n_dims,
+        int                   mode,
+        int                   n_ctx,
+        float                 freq_base,
+        float                 freq_scale) {
+    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, false);
+}
+
 struct ggml_tensor * ggml_rope_custom_inplace(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
@@ -6779,7 +6799,7 @@ struct ggml_tensor * ggml_rope_back(
     struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
 
     int32_t params[] = { n_past, n_dims, mode, n_ctx };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_ROPE_BACK;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6810,7 +6830,7 @@ struct ggml_tensor * ggml_alibi(
 
     int32_t op_params[3] = { n_past, n_head };
     memcpy(op_params + 2, &bias_max, sizeof(float));
-    ggml_set_op_params(result, &op_params, sizeof(op_params));
+    ggml_set_op_params(result, op_params, sizeof(op_params));
 
     result->op   = GGML_OP_ALIBI;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6837,7 +6857,7 @@ struct ggml_tensor * ggml_clamp(
     struct ggml_tensor * result = ggml_view_tensor(ctx, a);
 
     float params[] = { min, max };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_CLAMP;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6872,10 +6892,10 @@ GGML_API struct ggml_tensor * ggml_conv_1d(
         ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0),
         a->ne[2], 1, 1,
     };
-    struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
 
     int32_t params[] = { s0, p0, d0 };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op = GGML_OP_CONV_1D;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6887,10 +6907,10 @@ GGML_API struct ggml_tensor * ggml_conv_1d(
 
 // ggml_conv_2d
 
-struct ggml_tensor* ggml_conv_2d(
-    struct ggml_context* ctx,
-    struct ggml_tensor * a,
-    struct ggml_tensor * b,
+struct ggml_tensor * ggml_conv_2d(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a,
+    struct ggml_tensor  * b,
     int                  s0,
     int                  s1,
     int                  p0,
@@ -6911,10 +6931,10 @@ struct ggml_tensor* ggml_conv_2d(
         ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1),
         a->ne[3], b->ne[3],
     };
-    struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
 
     int32_t params[] = { s0, s1, p0, p1, d0, d1 };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op = GGML_OP_CONV_2D;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6927,7 +6947,7 @@ struct ggml_tensor* ggml_conv_2d(
 
 // ggml_conv_1d_ph
 
-struct ggml_tensor* ggml_conv_1d_ph(
+struct ggml_tensor * ggml_conv_1d_ph(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
@@ -6945,7 +6965,7 @@ static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) {
 
 // ggml_pool_1d
 
-struct ggml_tensor* ggml_pool_1d(
+struct ggml_tensor * ggml_pool_1d(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         enum ggml_op_pool     op,
@@ -6964,10 +6984,10 @@ struct ggml_tensor* ggml_pool_1d(
         ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
         a->ne[1],
     };
-    struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
 
     int32_t params[] = { op, k0, s0, p0 };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op = GGML_OP_POOL_1D;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6978,7 +6998,7 @@ struct ggml_tensor* ggml_pool_1d(
 
 // ggml_pool_2d
 
-struct ggml_tensor* ggml_pool_2d(
+struct ggml_tensor * ggml_pool_2d(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         enum ggml_op_pool     op,
@@ -7001,10 +7021,10 @@ struct ggml_tensor* ggml_pool_2d(
         ggml_calc_pool_output_size(a->ne[1], k1, s1, p1),
         a->ne[2],
     };
-    struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
 
     int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op = GGML_OP_POOL_2D;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -7172,7 +7192,7 @@ struct ggml_tensor * ggml_win_part(
     struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
 
     int32_t params[] = { npx, npy, w };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_WIN_PART;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -7202,7 +7222,7 @@ struct ggml_tensor * ggml_win_unpart(
     struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
 
     int32_t params[] = { w };
-    ggml_set_op_params(result, &params, sizeof(params));
+    ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_WIN_UNPART;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -7226,7 +7246,7 @@ static struct ggml_tensor * ggml_unary_impl(
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
-    ggml_set_unary_op(result, op);
+    ggml_set_op_params_i32(result, 0, (int32_t) op);
 
     result->op   = GGML_OP_UNARY;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -7331,7 +7351,7 @@ struct ggml_tensor * ggml_map_binary_inplace_f32(
     return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
 }
 
-// ggml_map_custom1
+// ggml_map_custom1_f32
 
 static struct ggml_tensor * ggml_map_custom1_impl_f32(
         struct ggml_context          * ctx,
@@ -7348,7 +7368,7 @@ static struct ggml_tensor * ggml_map_custom1_impl_f32(
 
     ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
 
-    result->op = GGML_OP_MAP_CUSTOM1;
+    result->op = GGML_OP_MAP_CUSTOM1_F32;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
 
@@ -7369,7 +7389,7 @@ struct ggml_tensor * ggml_map_custom1_inplace_f32(
     return ggml_map_custom1_impl_f32(ctx, a, fun, true);
 }
 
-// ggml_map_custom2
+// ggml_map_custom2_f32
 
 static struct ggml_tensor * ggml_map_custom2_impl_f32(
         struct ggml_context          * ctx,
@@ -7387,7 +7407,7 @@ static struct ggml_tensor * ggml_map_custom2_impl_f32(
 
     ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
 
-    result->op = GGML_OP_MAP_CUSTOM2;
+    result->op = GGML_OP_MAP_CUSTOM2_F32;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
     result->src[1] = b;
@@ -7411,7 +7431,7 @@ struct ggml_tensor * ggml_map_custom2_inplace_f32(
     return ggml_map_custom2_impl_f32(ctx, a, b, fun, true);
 }
 
-// ggml_map_custom3
+// ggml_map_custom3_f32
 
 static struct ggml_tensor * ggml_map_custom3_impl_f32(
         struct ggml_context          * ctx,
@@ -7430,7 +7450,7 @@ static struct ggml_tensor * ggml_map_custom3_impl_f32(
 
     ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
 
-    result->op = GGML_OP_MAP_CUSTOM3;
+    result->op = GGML_OP_MAP_CUSTOM3_F32;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
     result->src[1] = b;
@@ -7457,6 +7477,190 @@ struct ggml_tensor * ggml_map_custom3_inplace_f32(
     return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true);
 }
 
+// ggml_map_custom1
+struct ggml_map_custom1_op_params {
+    ggml_custom1_op_t fun;
+    int n_tasks;
+    void * userdata;
+};
+
+static struct ggml_tensor * ggml_map_custom1_impl(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        const  ggml_custom1_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata,
+        bool                           inplace) {
+    GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
+
+    bool is_node = false;
+
+    if (!inplace && a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    struct ggml_map_custom1_op_params params = {
+        /*.fun      =*/ fun,
+        /*.n_tasks  =*/ n_tasks,
+        /*.userdata =*/ userdata
+    };
+    ggml_set_op_params(result, (const void *) &params, sizeof(params));
+
+    result->op = GGML_OP_MAP_CUSTOM1;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_custom1(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        const  ggml_custom1_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, false);
+}
+
+struct ggml_tensor * ggml_map_custom1_inplace(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        const  ggml_custom1_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, true);
+}
+
+// ggml_map_custom2
+
+struct ggml_map_custom2_op_params {
+    ggml_custom2_op_t fun;
+    int n_tasks;
+    void * userdata;
+};
+
+static struct ggml_tensor * ggml_map_custom2_impl(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        const  ggml_custom2_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata,
+        bool                           inplace) {
+    GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    struct ggml_map_custom2_op_params params = {
+        /*.fun      =*/ fun,
+        /*.n_tasks  =*/ n_tasks,
+        /*.userdata =*/ userdata
+    };
+    ggml_set_op_params(result, (const void *) &params, sizeof(params));
+
+    result->op = GGML_OP_MAP_CUSTOM2;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_custom2(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        const  ggml_custom2_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, false);
+}
+
+struct ggml_tensor * ggml_map_custom2_inplace(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        const  ggml_custom2_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true);
+}
+
+// ggml_map_custom3
+
+struct ggml_map_custom3_op_params {
+    ggml_custom3_op_t fun;
+    int n_tasks;
+    void * userdata;
+};
+
+static struct ggml_tensor * ggml_map_custom3_impl(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        struct ggml_tensor           * c,
+        const  ggml_custom3_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata,
+        bool                           inplace) {
+    GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad || c->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    struct ggml_map_custom3_op_params params = {
+        /*.fun      =*/ fun,
+        /*.n_tasks  =*/ n_tasks,
+        /*.userdata =*/ userdata
+    };
+    ggml_set_op_params(result, (const void *) &params, sizeof(params));
+
+    result->op = GGML_OP_MAP_CUSTOM3;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+    result->src[2] = c;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_custom3(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        struct ggml_tensor           * c,
+        const  ggml_custom3_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, false);
+}
+
+struct ggml_tensor * ggml_map_custom3_inplace(
+        struct ggml_context          * ctx,
+        struct ggml_tensor           * a,
+        struct ggml_tensor           * b,
+        struct ggml_tensor           * c,
+        const  ggml_custom3_op_t       fun,
+        int                            n_tasks,
+        void                         * userdata) {
+    return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true);
+}
+
+
+
 // ggml_cross_entropy_loss
 
 struct ggml_tensor * ggml_cross_entropy_loss(
@@ -9265,8 +9469,8 @@ static void ggml_compute_forward_sum_rows_f32(
     for (int64_t i3 = 0; i3 < ne03; i3++) {
         for (int64_t i2 = 0; i2 < ne02; i2++) {
             for (int64_t i1 = 0; i1 < ne01; i1++) {
-                float* src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03);
-                float* dst_row = (float *) ((char *) dst->data  + i1*nb1  + i2*nb2  + i3*nb3);
+                float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03);
+                float * dst_row = (float *) ((char *) dst->data  + i1*nb1  + i2*nb2  + i3*nb3);
                 float row_sum = 0;
                 ggml_vec_sum_f32(ne00, &row_sum, src_row);
                 dst_row[0] = row_sum;
@@ -10523,71 +10727,95 @@ static void ggml_compute_forward_mul_mat(
         return;
     }
 
-    // parallelize by src0 rows
-    const int64_t dr = (ne01 + nth - 1)/nth;
+    const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
+    const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
 
-    const int64_t ir10 = dr*ith;
-    const int64_t ir11 = MIN(ir10 + dr, ne01);
+    const int64_t nr0 = ne01;           // src0 rows
+    const int64_t nr1 = ne11*ne12*ne13; // src1 rows
 
-    // src1 rows
-    const int64_t nr1 = ne11*ne12*ne13;
+    //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
 
-    const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
-    const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
+    // distribute the thread work across the inner or outer loop based on which one is larger
 
-    for (int64_t ir1 = 0; ir1 < nr1; ++ir1) {
-        const int64_t i13 = (ir1/(ne12*ne11));
-        const int64_t i12 = (ir1 - i13*ne12*ne11)/ne11;
-        const int64_t i11 = (ir1 - i13*ne12*ne11 - i12*ne11);
-
-        const int64_t ir0 = (ir1/ne11)%(ne02*ne03);
-        const int64_t i03 = (ir0/(ne02));
-        // Hack for "Falcon multi-query-attention key stutter" / alternative to ggml_repeat2.
-        // See https://github.com/ggerganov/llama.cpp/issues/1602#issuecomment-1606087470:
-        // GG: this is likely the correct way to broadcast, though need some more thought
-        //     therefore leaving the comments to remind us for now
-        const int64_t i02 = (i12 / (ne12 / ne02));
-        // Original from PR/224 (and also essential/correct for non-broadcast matmuls in Falcon)
-        // const int64_t i02 = (ir0 - i03*ne02);
-
-        const int64_t i1 = i11;
-        const int64_t i2 = i12;
-        const int64_t i3 = i13;
-
-        const char * src0_row = (const char *) src0->data + (  0 + i02*nb02 + i03*nb03     );
-
-        // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
-        //       if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
-        //       the original src1 data pointer, so we should index using the indices directly
-        // TODO: this is a bit of a hack, we should probably have a better way to handle this
-        const char * src1_col = (const char *) wdata +
-            (src1_cont || src1->type != vec_dot_type
-             ? (i11      + i12*ne11 + i13*ne12*ne11)*row_size
-             : (i11*nb11 + i12*nb12 + i13*nb13));
-
-        float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
-
-        for (int64_t ir = ir10; ir < ir11; ++ir) {
-            vec_dot(ne00, &dst_col[ir], src0_row + ir*nb01, src1_col);
-        }
+    const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
+    const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows
+
+    const int64_t ith0 = ith % nth0;
+    const int64_t ith1 = ith / nth0;
+
+    const int64_t dr0 = (nr0 + nth0 - 1)/nth0;
+    const int64_t dr1 = (nr1 + nth1 - 1)/nth1;
+
+    const int64_t ir010 = dr0*ith0;
+    const int64_t ir011 = MIN(ir010 + dr0, nr0);
+
+    const int64_t ir110 = dr1*ith1;
+    const int64_t ir111 = MIN(ir110 + dr1, nr1);
+
+    //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);
+
+    // threads with no work simply yield (not sure if it helps)
+    if (ir010 >= ir011 || ir110 >= ir111) {
+        sched_yield();
+        return;
     }
 
-    //int64_t t1 = ggml_time_us();
-    //static int64_t acc = 0;
-    //acc += t1 - t0;
-    //if (t1 - t0 > 10) {
-    //    printf("\n");
-    //    printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
-    //    printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
-    //    printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
+    assert(ne12 % ne02 == 0);
+    assert(ne13 % ne03 == 0);
 
-    //    printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
-    //}
-}
+    // broadcast factors
+    const int64_t r2 = ne12/ne02;
+    const int64_t r3 = ne13/ne03;
 
+    // block-tiling attempt
+    const int64_t blck_0 = 16;
+    const int64_t blck_1 = 16;
 
-// ggml_compute_forward_out_prod
+    // attempt to reduce false-sharing (does not seem to make a difference)
+    float tmp[16];
+
+    for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
+        for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
+            for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
+                const int64_t i13 = (ir1/(ne12*ne11));
+                const int64_t i12 = (ir1 - i13*ne12*ne11)/ne11;
+                const int64_t i11 = (ir1 - i13*ne12*ne11 - i12*ne11);
+
+                // broadcast src0 into src1
+                const int64_t i03 = i13/r3;
+                const int64_t i02 = i12/r2;
+
+                const int64_t i1 = i11;
+                const int64_t i2 = i12;
+                const int64_t i3 = i13;
+
+                const char * src0_row = (const char *) src0->data + (0 + i02*nb02 + i03*nb03);
 
+                // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
+                //       if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
+                //       the original src1 data pointer, so we should index using the indices directly
+                // TODO: this is a bit of a hack, we should probably have a better way to handle this
+                const char * src1_col = (const char *) wdata +
+                    (src1_cont || src1->type != vec_dot_type
+                     ? (i11      + i12*ne11 + i13*ne12*ne11)*row_size
+                     : (i11*nb11 + i12*nb12 + i13*nb13));
+
+                float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
+
+                //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
+                //    vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
+                //}
+
+                for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
+                    vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col);
+                }
+                memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
+            }
+        }
+    }
+}
+
+// ggml_compute_forward_out_prod
 
 static void ggml_compute_forward_out_prod_f32(
         const struct ggml_compute_params * params,
@@ -12871,7 +13099,7 @@ static void ggml_compute_forward_pool_1d(
         const struct ggml_tensor * src0,
               struct ggml_tensor * dst) {
 
-    const int32_t* opts = (const int32_t*)dst->op_params;
+    const int32_t * opts = (const int32_t *)dst->op_params;
     enum ggml_op_pool op = opts[0];
     const int k0 = opts[1];
     const int s0 = opts[2];
@@ -14204,24 +14432,6 @@ static void ggml_compute_forward_map_custom1_f32(
     fun(dst, a);
 }
 
-
-static void ggml_compute_forward_map_custom1(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        struct ggml_tensor * dst,
-        const ggml_custom1_op_f32_t fun) {
-    switch (a->type) {
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_map_custom1_f32(params, a, dst, fun);
-            } break;
-        default:
-            {
-                GGML_ASSERT(false);
-            } break;
-    }
-}
-
 // ggml_compute_forward_map_custom2
 
 static void ggml_compute_forward_map_custom2_f32(
@@ -14240,24 +14450,6 @@ static void ggml_compute_forward_map_custom2_f32(
 }
 
 
-static void ggml_compute_forward_map_custom2(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b,
-        struct ggml_tensor * dst,
-        const ggml_custom2_op_f32_t fun) {
-    switch (a->type) {
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_map_custom2_f32(params, a, b, dst, fun);
-            } break;
-        default:
-            {
-                GGML_ASSERT(false);
-            } break;
-    }
-}
-
 // ggml_compute_forward_map_custom3
 
 static void ggml_compute_forward_map_custom3_f32(
@@ -14276,24 +14468,52 @@ static void ggml_compute_forward_map_custom3_f32(
     fun(dst, a, b, c);
 }
 
+// ggml_compute_forward_map_custom1
+
+static void ggml_compute_forward_map_custom1(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * a,
+              struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params;
+
+    p->fun(dst, a, params->ith, params->nth, p->userdata);
+}
+
+// ggml_compute_forward_map_custom2
+
+static void ggml_compute_forward_map_custom2(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * a,
+        const struct ggml_tensor * b,
+              struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params;
+
+    p->fun(dst, a, b, params->ith, params->nth, p->userdata);
+}
+
+// ggml_compute_forward_map_custom3
 
 static void ggml_compute_forward_map_custom3(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * a,
         const struct ggml_tensor * b,
         const struct ggml_tensor * c,
-        struct ggml_tensor * dst,
-        const ggml_custom3_op_f32_t fun) {
-    switch (a->type) {
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_map_custom3_f32(params, a, b, c, dst, fun);
-            } break;
-        default:
-            {
-                GGML_ASSERT(false);
-            } break;
+              struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
     }
+
+    struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params;
+
+    p->fun(dst, a, b, c, params->ith, params->nth, p->userdata);
 }
 
 // ggml_compute_forward_cross_entropy_loss
@@ -14815,25 +15035,40 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
                 ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun);
             }
             break;
-        case GGML_OP_MAP_CUSTOM1:
+        case GGML_OP_MAP_CUSTOM1_F32:
             {
                 ggml_custom1_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom1(params, tensor->src[0], tensor, fun);
+                ggml_compute_forward_map_custom1_f32(params, tensor->src[0], tensor, fun);
             }
             break;
-        case GGML_OP_MAP_CUSTOM2:
+        case GGML_OP_MAP_CUSTOM2_F32:
             {
                 ggml_custom2_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor, fun);
+                ggml_compute_forward_map_custom2_f32(params, tensor->src[0], tensor->src[1], tensor, fun);
             }
             break;
-        case GGML_OP_MAP_CUSTOM3:
+        case GGML_OP_MAP_CUSTOM3_F32:
             {
                 ggml_custom3_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun);
+                ggml_compute_forward_map_custom3_f32(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun);
+            }
+            break;
+        case GGML_OP_MAP_CUSTOM1:
+            {
+                ggml_compute_forward_map_custom1(params, tensor->src[0], tensor);
+            }
+            break;
+        case GGML_OP_MAP_CUSTOM2:
+            {
+                ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor);
+            }
+            break;
+        case GGML_OP_MAP_CUSTOM3:
+            {
+                ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
             }
             break;
         case GGML_OP_CROSS_ENTROPY_LOSS:
@@ -15641,6 +15876,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             } break;
         case GGML_OP_MAP_UNARY:
         case GGML_OP_MAP_BINARY:
+        case GGML_OP_MAP_CUSTOM1_F32:
+        case GGML_OP_MAP_CUSTOM2_F32:
+        case GGML_OP_MAP_CUSTOM3_F32:
         case GGML_OP_MAP_CUSTOM1:
         case GGML_OP_MAP_CUSTOM2:
         case GGML_OP_MAP_CUSTOM3:
@@ -15825,6 +16063,35 @@ struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cg
     return result;
 }
 
+struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) {
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, GGML_GRAPH_SIZE);
+    struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs);
+
+    *cgraph = (struct ggml_cgraph) {
+        /*.n_nodes      =*/ 0,
+        /*.n_leafs      =*/ 0,
+        /*.nodes        =*/ { NULL },
+        /*.grads        =*/ { NULL },
+        /*.leafs        =*/ { NULL },
+        /*.hash_table   =*/ { NULL },
+        /*.perf_runs    =*/ 0,
+        /*.perf_cycles  =*/ 0,
+        /*.perf_time_us =*/ 0,
+    };
+
+    return cgraph;
+}
+
+struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor) {
+    struct ggml_cgraph * cgraph = ggml_new_graph(ctx);
+    ggml_build_forward_impl(cgraph, tensor, false);
+    return cgraph;
+}
+
+size_t ggml_graph_overhead(void) {
+    return GGML_OBJECT_SIZE + GGML_PAD(GGML_GRAPH_SIZE, GGML_MEM_ALIGN);
+}
+
 //
 // thread data
 //
@@ -16401,11 +16668,38 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
             case GGML_OP_WIN_UNPART:
             case GGML_OP_MAP_UNARY:
             case GGML_OP_MAP_BINARY:
+            case GGML_OP_MAP_CUSTOM1_F32:
+            case GGML_OP_MAP_CUSTOM2_F32:
+            case GGML_OP_MAP_CUSTOM3_F32:
+                {
+                    n_tasks = 1;
+                } break;
             case GGML_OP_MAP_CUSTOM1:
+                {
+                    struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params;
+                    if (p->n_tasks == GGML_N_TASKS_MAX) {
+                        n_tasks = n_threads;
+                    } else {
+                        n_tasks = MIN(p->n_tasks, n_threads);
+                    }
+                } break;
             case GGML_OP_MAP_CUSTOM2:
+                {
+                    struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params;
+                    if (p->n_tasks == GGML_N_TASKS_MAX) {
+                        n_tasks = n_threads;
+                    } else {
+                        n_tasks = MIN(p->n_tasks, n_threads);
+                    }
+                } break;
             case GGML_OP_MAP_CUSTOM3:
                 {
-                    n_tasks = 1;
+                    struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params;
+                    if (p->n_tasks == GGML_N_TASKS_MAX) {
+                        n_tasks = n_threads;
+                    } else {
+                        n_tasks = MIN(p->n_tasks, n_threads);
+                    }
                 } break;
             case GGML_OP_CROSS_ENTROPY_LOSS:
                 {
@@ -16544,10 +16838,9 @@ void ggml_graph_reset(struct ggml_cgraph * cgraph) {
 void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
     struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads);
 
-    struct ggml_tensor * buf = ggml_new_tensor_1d(ctx, GGML_TYPE_I8, cplan.work_size);
-    GGML_ASSERT(buf);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
 
-    cplan.work_data = buf->data;
+    cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
 
     ggml_graph_compute(cgraph, &cplan);
 }
diff --git a/ggml.h b/ggml.h
index 0d4a4b297f33ed19a0735e3ab870324f36d5195a..bdbd12800433242dec1e6dac8c96875540dd19e7 100644
--- a/ggml.h
+++ b/ggml.h
@@ -183,6 +183,15 @@
 #    define GGML_API
 #endif
 
+// TODO: support for clang
+#ifdef __GNUC__
+#    define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
+#elif defined(_MSC_VER)
+#    define GGML_DEPRECATED(func, hint) __declspec(deprecated(hint)) func
+#else
+#    define GGML_DEPRECATED(func, hint) func
+#endif
+
 #include <stdint.h>
 #include <stddef.h>
 #include <stdbool.h>
@@ -208,6 +217,8 @@
 
 #define GGML_UNUSED(x) (void)(x)
 
+#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
+
 #define GGML_ASSERT(x) \
     do { \
         if (!(x)) { \
@@ -372,6 +383,10 @@ extern "C" {
         GGML_OP_MAP_UNARY,
         GGML_OP_MAP_BINARY,
 
+        GGML_OP_MAP_CUSTOM1_F32,
+        GGML_OP_MAP_CUSTOM2_F32,
+        GGML_OP_MAP_CUSTOM3_F32,
+
         GGML_OP_MAP_CUSTOM1,
         GGML_OP_MAP_CUSTOM2,
         GGML_OP_MAP_CUSTOM3,
@@ -395,6 +410,12 @@ extern "C" {
         GGML_UNARY_OP_SILU,
     };
 
+    enum ggml_object_type {
+        GGML_OBJECT_TENSOR,
+        GGML_OBJECT_GRAPH,
+        GGML_OBJECT_WORK_BUFFER
+    };
+
     // ggml object
     struct ggml_object {
         size_t offs;
@@ -402,7 +423,9 @@ extern "C" {
 
         struct ggml_object * next;
 
-        char padding[8];
+        enum ggml_object_type type;
+
+        char padding[4];
     };
 
     static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
@@ -423,7 +446,7 @@ extern "C" {
         enum ggml_op op;
 
         // op params - allocated as int32_t for alignment
-        int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(uint32_t)];
+        int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
 
         bool is_param;
 
@@ -484,6 +507,8 @@ extern "C" {
         int64_t perf_time_us;
     };
 
+    static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph);
+
     // scratch buffer
     struct ggml_scratch {
         size_t offs;
@@ -558,6 +583,8 @@ extern "C" {
     GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
     GGML_API bool ggml_is_permuted  (const struct ggml_tensor * tensor);
 
+    GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
+
     // use this to compute the memory overhead of a tensor
     GGML_API size_t ggml_tensor_overhead(void);
 
@@ -1158,7 +1185,18 @@ extern "C" {
             int                   mode,
             int                   n_ctx);
 
-    // custom RoPE, in-place, returns view(a)
+    // custom RoPE
+    GGML_API struct ggml_tensor * ggml_rope_custom(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx,
+            float                 freq_base,
+            float                 freq_scale);
+
+    // in-place, returns view(a)
     GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1217,7 +1255,7 @@ extern "C" {
 
     // conv_1d with padding = half
     // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
-    GGML_API struct ggml_tensor* ggml_conv_1d_ph(
+    GGML_API struct ggml_tensor * ggml_conv_1d_ph(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             struct ggml_tensor  * b,
@@ -1230,7 +1268,7 @@ extern "C" {
         GGML_OP_POOL_COUNT,
     };
 
-    GGML_API struct ggml_tensor* ggml_pool_1d(
+    GGML_API struct ggml_tensor * ggml_pool_1d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             enum ggml_op_pool     op,
@@ -1238,7 +1276,7 @@ extern "C" {
             int                   s0, // stride
             int                   p0); // padding
 
-    GGML_API struct ggml_tensor* ggml_pool_2d(
+    GGML_API struct ggml_tensor * ggml_pool_2d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             enum ggml_op_pool     op,
@@ -1292,15 +1330,6 @@ extern "C" {
             int                   h0,
             int                   w);
 
-    // custom operators
-
-    typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
-    typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
-
-    typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
-    typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
-    typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
-
     GGML_API struct ggml_tensor * ggml_unary(
             struct ggml_context * ctx,
              struct ggml_tensor * a,
@@ -1311,63 +1340,138 @@ extern "C" {
         struct ggml_tensor  * a,
         enum ggml_unary_op op);
 
-    GGML_API struct ggml_tensor * ggml_map_unary_f32(
+    // custom operators
+
+    typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
+    typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
+
+    typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
+    typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
+    typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32(
             struct ggml_context        * ctx,
             struct ggml_tensor         * a,
-                   ggml_unary_op_f32_t   fun);
+                   ggml_unary_op_f32_t   fun),
+        "use ggml_map_custom1 instead");
 
-    GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
             struct ggml_context        * ctx,
             struct ggml_tensor         * a,
-                   ggml_unary_op_f32_t   fun);
+                   ggml_unary_op_f32_t   fun),
+        "use ggml_map_custom1_inplace instead");
 
-    GGML_API struct ggml_tensor * ggml_map_binary_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32(
             struct ggml_context         * ctx,
             struct ggml_tensor          * a,
             struct ggml_tensor          * b,
-                   ggml_binary_op_f32_t   fun);
+                   ggml_binary_op_f32_t   fun),
+        "use ggml_map_custom2 instead");
 
-    GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
             struct ggml_context         * ctx,
             struct ggml_tensor          * a,
             struct ggml_tensor          * b,
-                   ggml_binary_op_f32_t   fun);
+                   ggml_binary_op_f32_t   fun),
+        "use ggml_map_custom2_inplace instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom1_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
-                   ggml_custom1_op_f32_t   fun);
+                   ggml_custom1_op_f32_t   fun),
+        "use ggml_map_custom1 instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
-                   ggml_custom1_op_f32_t   fun);
+                   ggml_custom1_op_f32_t   fun),
+        "use ggml_map_custom1_inplace instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom2_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
             struct ggml_tensor           * b,
-                   ggml_custom2_op_f32_t   fun);
+                   ggml_custom2_op_f32_t   fun),
+        "use ggml_map_custom2 instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
             struct ggml_tensor           * b,
-                   ggml_custom2_op_f32_t   fun);
+                   ggml_custom2_op_f32_t   fun),
+        "use ggml_map_custom2_inplace instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom3_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
             struct ggml_tensor           * b,
             struct ggml_tensor           * c,
-                   ggml_custom3_op_f32_t   fun);
+                   ggml_custom3_op_f32_t   fun),
+        "use ggml_map_custom3 instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
             struct ggml_tensor           * b,
             struct ggml_tensor           * c,
-                   ggml_custom3_op_f32_t   fun);
+                   ggml_custom3_op_f32_t   fun),
+        "use ggml_map_custom3_inplace instead");
+
+    // custom operators v2
+
+    typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
+    typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
+    typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
+
+    #define GGML_N_TASKS_MAX -1
+
+    GGML_API struct ggml_tensor * ggml_map_custom1(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            ggml_custom1_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom1_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            ggml_custom1_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom2(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            ggml_custom2_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom2_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            ggml_custom2_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom3(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            struct ggml_tensor    * c,
+            ggml_custom3_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom3_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            struct ggml_tensor    * c,
+            ggml_custom3_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
 
     // loss function
 
@@ -1390,11 +1494,17 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * tensor);
 
+
     GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
 
     GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
     GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
 
+    // graph allocation in a context
+    GGML_API struct ggml_cgraph * ggml_new_graph        (struct ggml_context * ctx);
+    GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor);
+    GGML_API size_t ggml_graph_overhead(void);
+
     // ggml_graph_plan() has to be called before ggml_graph_compute()
     // when plan.work_size > 0, caller must allocate memory for plan.work_data
     GGML_API struct ggml_cplan ggml_graph_plan   (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 4c37dd7e511da4faf009fc42dd8a1da7e9d9fe2f..a2283269c0525a682319cb5d5cdef69bdf62515f 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -243,7 +243,7 @@ void sample_temperature(llama_token_data_array * candidates_p, float temp)
     if (temp <= 0)
     {
         // Imitate greedy sampling
-        temp = 0.01f; //cannot be zero else div0
+        temp = 0.00390625f; //cannot be zero else div0, this is 1/256
         llama_sample_temperature(nullptr, candidates_p, temp);
         llama_sample_top_k(nullptr, candidates_p, 1, 1); //only want first candidate
     }
@@ -347,6 +347,10 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
     debugmode = inputs.debugmode;
     unbanTokens = inputs.unban_tokens;
     blasbatchsize = inputs.blasbatchsize;
+    if(blasbatchsize<=0)
+    {
+        blasbatchsize = 8;
+    }
     params.memory_f16 = inputs.f16_kv;
     params.n_ctx = inputs.max_context_length;
 
@@ -374,7 +378,8 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
         else
         {
             //approximate NTK aware ctx
-            rope_freq_base = (params.n_ctx <= 3072 ? 26000.0f : (params.n_ctx <= 4096 ? 32000.0f : (params.n_ctx <= 6144 ? 54000.0f : 82684.0f)));
+            rope_freq_base = (params.n_ctx <= 3072 ? 26000.0f : (params.n_ctx <= 4096 ? 32000.0f : (params.n_ctx <= 6144 ? 54000.0f : (params.n_ctx <= 8192 ? 82684.0f : (params.n_ctx <= 12288 ? 140000.0f : 200000.0f)))));
+
         }
 
         printf("Using automatic RoPE scaling (scale:%.3f, base:%.1f)\n",rope_freq_scale,rope_freq_base);
@@ -466,6 +471,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
         llama_ctx_params.seed = -1;
         llama_ctx_params.f16_kv = inputs.f16_kv;
         llama_ctx_params.low_vram = inputs.low_vram;
+        llama_ctx_params.mul_mat_q = inputs.use_mmq;
         llama_ctx_params.logits_all = false;
         llama_ctx_params.use_mmap = inputs.use_mmap;
         llama_ctx_params.use_mlock = inputs.use_mlock;
diff --git a/grammars/json.gbnf b/grammars/json.gbnf
index 40fa2b63732554acc2c37d79f54091edfa7df2ab..a9537cdf9fbe49c79967090eab759973c52f2136 100644
--- a/grammars/json.gbnf
+++ b/grammars/json.gbnf
@@ -1,29 +1,25 @@
-# Grammar for subset of JSON - doesn't support full string or number syntax
-
-root  ::= object
-value ::= object | array | string | number | boolean | "null"
+root   ::= object
+value  ::= object | array | string | number | ("true" | "false" | "null") ws
 
 object ::=
   "{" ws (
             string ":" ws value
     ("," ws string ":" ws value)*
-  )? "}"
+  )? "}" ws
 
 array  ::=
   "[" ws (
             value
     ("," ws value)*
-  )? "]"
+  )? "]" ws
 
-string  ::=
+string ::=
   "\"" (
     [^"\\] |
     "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
   )* "\"" ws
 
-# Only plain integers currently
-number  ::= "-"? [0-9]+ ws
-boolean ::= ("true" | "false") ws
+number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
 
 # Optional space: by convention, applied in this grammar after literal chars when allowed
 ws ::= ([ \t\n] ws)?
diff --git a/k_quants.c b/k_quants.c
index e792c57acc7b7b4793b752a3a8501a482cd12e59..6348fce6b94d031071302b40c10c5113850f473c 100644
--- a/k_quants.c
+++ b/k_quants.c
@@ -39,6 +39,8 @@
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 
+#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
+
 //
 // 2-6 bit quantization in super-blocks
 //
@@ -1353,7 +1355,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
         const __m256i all_scales = _mm256_cvtepi8_epi16(scales8);
         const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0);
         const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1);
-        const __m256i scales[2] = {_mm256_set_m128i(l_scales, l_scales), _mm256_set_m128i(h_scales, h_scales)};
+        const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)};
 
         __m256i sumi = _mm256_setzero_si256();
 
@@ -1421,7 +1423,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
         const __m128i summs_1 = _mm_madd_epi16(mins_1, _mm_loadu_si128((const __m128i*)&y[i].bsums[8]));
 
         // sumf += -dmin * summs in 32bits*8
-        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(_mm256_set_m128i(summs_1, summs_0))), acc);
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(MM256_SET_M128I(summs_1, summs_0))), acc);
 
         const __m128i scales_0 = _mm_cvtepi8_epi16(scales16);
         const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales16, scales16));
@@ -1493,7 +1495,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
         }
 
         // sumf += dall * isum - dmin * summs in 32bits
-        __m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
+        __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
         acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dall), _mm256_cvtepi32_ps(sumi)), acc);
     }
 
@@ -1644,8 +1646,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
         summs += dmin * smin;
 
         const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2);
-        const __m256i q2_0 = _mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q2bits, 2), q2bits), m3);
-        const __m256i q2_1 = _mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3);
+        const __m256i q2_0 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 2), q2bits), m3);
+        const __m256i q2_1 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3);
 
         const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
         const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
@@ -1709,10 +1711,10 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
         const __m128i p2 = _mm_maddubs_epi16(q2_2, _mm256_extractf128_si256(q8_1, 0));
         const __m128i p3 = _mm_maddubs_epi16(q2_3, _mm256_extractf128_si256(q8_1, 1));
 
-        const __m256i p_0 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0));
-        const __m256i p_1 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1));
-        const __m256i p_2 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2));
-        const __m256i p_3 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3));
+        const __m256i p_0 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0));
+        const __m256i p_1 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1));
+        const __m256i p_2 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2));
+        const __m256i p_3 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3));
 
         acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0)), acc);
         acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1)), acc);
@@ -1917,7 +1919,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
         const __m256i all_scales = _mm256_cvtepi8_epi16(scales128);
         const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0);
         const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1);
-        const __m256i scales[2] = {_mm256_set_m128i(l_scales, l_scales), _mm256_set_m128i(h_scales, h_scales)};
+        const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)};
 
         // high bit
         const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].hmask);
@@ -2128,7 +2130,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
         }
 
         // multiply with block scale and accumulate
-        __m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
+        __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
         acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc);
 
     }
@@ -2303,13 +2305,13 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
         aux16[0] = a & 0x0f0f;
         aux16[1] = (a >> 4) & 0x0f0f;
 
-        const __m256i scale_0 = _mm256_set_m128i(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8));
-        const __m256i scale_1 = _mm256_set_m128i(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8));
+        const __m256i scale_0 = MM256_SET_M128I(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8));
+        const __m256i scale_1 = MM256_SET_M128I(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8));
 
         memcpy(&aux64, x[i].hmask, 8);
 
         const __m128i haux = _mm_set_epi64x(aux64 >> 1, aux64 >> 0);
-        __m256i q3h_0 = _mm256_set_m128i(_mm_srli_epi16(haux, 2), haux);
+        __m256i q3h_0 = MM256_SET_M128I(_mm_srli_epi16(haux, 2), haux);
         __m256i q3h_1 = _mm256_srli_epi16(q3h_0, 4);
         q3h_0 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_0, m1), 2);
         q3h_1 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_1, m1), 2);
@@ -2318,7 +2320,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
         const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3);
 
         // prepare low and high bits
-        const __m256i q3aux  = _mm256_set_m128i(_mm_srli_epi16(q3bits, 2), q3bits);
+        const __m256i q3aux  = MM256_SET_M128I(_mm_srli_epi16(q3bits, 2), q3bits);
         const __m256i q3l_0 = _mm256_and_si256(q3aux, m3);
         const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3aux, 4), m3);
 
@@ -2429,7 +2431,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
         p16_0 = _mm_add_epi32(p16_0, p16_2);
         p16_1 = _mm_add_epi32(p16_1, p16_3);
-        __m256i p16 = _mm256_set_m128i(p16_1, p16_0);
+        __m256i p16 = MM256_SET_M128I(p16_1, p16_0);
 
         // multiply with block scale and accumulate
         acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16)), acc);
@@ -2620,7 +2622,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
         acc_m = _mm_fmadd_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod), acc_m);
 
         const __m128i sc128  = _mm256_extracti128_si256(mins_and_scales, 0);
-        const __m256i scales = _mm256_set_m128i(sc128, sc128);
+        const __m256i scales = MM256_SET_M128I(sc128, sc128);
 
         __m256i sumi = _mm256_setzero_si256();
 
@@ -2727,7 +2729,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
         }
 
         __m256 vd = _mm256_set1_ps(d);
-        __m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
+        __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
         acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc);
 
     }
@@ -2968,11 +2970,11 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
 
         const __m128i p32_0 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_0);
         const __m128i p32_1 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_1);
-        acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(_mm256_set_m128i(p32_1, p32_0))), acc);
+        acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_1, p32_0))), acc);
 
         const __m128i p32_2 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_2);
         const __m128i p32_3 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_3);
-        acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(_mm256_set_m128i(p32_3, p32_2))), acc);
+        acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_3, p32_2))), acc);
 
     }
 
@@ -3160,7 +3162,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
         summs += dmin * _mm_extract_epi32(hsum, 0);
 
         const __m128i sc128  = _mm256_extracti128_si256(mins_and_scales, 0);
-        const __m256i scales = _mm256_set_m128i(sc128, sc128);
+        const __m256i scales = MM256_SET_M128I(sc128, sc128);
 
         const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].qh);
         __m256i hmask = mone;
@@ -3299,7 +3301,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
         }
 
         __m256 vd = _mm256_set1_ps(d);
-        __m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
+        __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
         acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc);
 
     }
@@ -3462,13 +3464,13 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
 
         const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
 
-        const __m256i scale_l = _mm256_set_m128i(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0]));
-        const __m256i scale_h = _mm256_set_m128i(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2]));
+        const __m256i scale_l = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0]));
+        const __m256i scale_h = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2]));
 
         int64_t aux64;
         memcpy(&aux64, x[i].qh, 8);
         const __m128i haux128 = _mm_set_epi64x(aux64 >> 1, aux64);
-        const __m256i haux256 = _mm256_set_m128i(_mm_srli_epi16(haux128, 2), haux128);
+        const __m256i haux256 = MM256_SET_M128I(_mm_srli_epi16(haux128, 2), haux128);
 
         const __m256i q5h_0 = _mm256_slli_epi16(_mm256_andnot_si256(haux256, mone), 4);
         const __m256i q5h_1 = _mm256_slli_epi16(_mm256_andnot_si256(_mm256_srli_epi16(haux256, 4), mone), 4);
@@ -3543,7 +3545,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
         const __m128i dot_0 = _mm_sub_epi32(_mm_add_epi32(p16_0, p16_2), _mm_add_epi32(s16_0, s16_2));
         const __m128i dot_1 = _mm_sub_epi32(_mm_add_epi32(p16_1, p16_3), _mm_add_epi32(s16_1, s16_3));
 
-        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_set_m128i(dot_1, dot_0))), acc);
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(dot_1, dot_0))), acc);
 
     }
 
@@ -3925,7 +3927,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
 
         }
 
-        __m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
+        __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
         acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc);
     }
 
@@ -4083,8 +4085,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
         const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4);
         const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh);
 
-        const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4);
-        const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4);
+        const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4);
+        const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4);
 
         const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0);
         const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_1);
@@ -4177,7 +4179,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
         sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2));
         sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3));
 
-        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(_mm256_set_m128i(sumi_1, sumi_0))), acc);
+        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi_1, sumi_0))), acc);
     }
 
     *s = hsum_float_8(acc);
diff --git a/klite.embd b/klite.embd
index 2824c89571af338ea69e18e3da0b01283654f3ec..153b4af7de47b5d7e0d62ba7308d043b84b5393b 100644
--- a/klite.embd
+++ b/klite.embd
@@ -1,6 +1,6 @@
 <!--
 An embedded version of Kobold Lite for use in koboldcpp
-Current version: 51
+Current version: 55
 Please go to https://github.com/LostRuins/lite.koboldai.net for updates on Kobold Lite.
 Kobold Lite is under the AGPL v3.0 License for the purposes of koboldcpp. Please do not remove this line.
 - Concedo
@@ -22,11 +22,11 @@ Kobold Lite is under the AGPL v3.0 License for the purposes of koboldcpp. Please
 	* Copyright 2011-2019 Twitter, Inc.
 	* Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
 	*//*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */html{font-family:sans-serif;-ms-text-size-adjust:100%;-webkit-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:none;text-decoration:underline;-webkit-text-decoration:underline dotted;-moz-text-decoration:underline dotted;text-decoration:underline dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{font-size:2em;margin:.67em 0}mark{background:#ff0;color:#000}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;height:0}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{color:inherit;font:inherit;margin:0}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{border:0;padding:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-appearance:textfield;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{border:1px solid silver;margin:0 2px;padding:.35em .625em .75em}legend{border:0;padding:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-collapse:collapse;border-spacing:0}td,th{padding:0}/*! 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Story",desc:"Starts a new game in story mode, using your current settings.",opmode:1,prefmodel1:["erebus","nerys","nerybus","janeway"],prefmodel2:["opt","wizard","vicuna","manticore","mantis","airoboros","chronos","alpaca"],prompt:"",memory:"",authorsnote:"",worldinfo:[]},{title:"New Adventure",desc:"Starts a new game in adventure mode, using your current settings.",opmode:2,prefmodel1:["nerys","nerybus","skein","adventure"],prefmodel2:["erebus","janeway","opt","wizard","vicuna","manticore","mantis","airoboros","chronos","alpaca"],prompt:"",adventure_context_mod:!0,memory:"",authorsnote:"",worldinfo:[]},{title:"New Chat",desc:"Starts a new game in chat mode, using your current settings.",opmode:3,chatname:"You",chatopponent:"KoboldAI",enhanced_chat_ui:!0,prefmodel1:["pygmalion-6","pygmalion-v8"],prefmodel2:["pygmalion","erebus","nerybus","opt"],prompt:"",memory:"",authorsnote:"",worldinfo:[]},{title:"New Instruct",desc:"Starts a new game in instruct mode, using your current settings.",opmode:4,instruct_starttag:"\\n### Instruction:\\n",instruct_endtag:"\\n### Response:\\n",prefmodel1:["alpaca","gpt4all","supercot","wizard","vicuna","manticore","mantis","airoboros","chronos"],prefmodel2:["erebus","nerys","nerybus","janeway","opt"],prompt:"",memory:"",authorsnote:"",worldinfo:[]},{title:"New Adventure (Alpaca)",author:"Henky!!",desc:"Starts a new game in adventure mode, with a prompt designed for Alpaca Instruct models. Begin by submitting a text describing the setting and your character. For the best experience avoid actions that make your goals to easy such as inputting the instant solution to your goals.",opmode:2,prefmodel1:["alpaca","gpt4all","supercot","wizard","vicuna","manticore","mantis","airoboros","chronos"],prefmodel2:["erebus","nerys","nerybus","janeway","opt"],prompt:"",adventure_context_mod:!1,memory:"### Instruction:\n\nSimulate a text adventure game.\nUser actions will be on their own seperate line prefixed with a >\n\nThe game will feature a brief introduction text about who the main character is and the setting of the world. Followed by a brief description of the current task that must be overcome. Afterwards it will be up to the user to input the desired instruction and outputs will describe the impact of this action.\n\nAll outputs should consider how likely it is that the players action succeeds and succeed or fail accordingly. The game should be challenging and action failures should be funny.\n\n### Response:",authorsnote:"",worldinfo:[]},{title:"KoboldGPT Chat",author:"Concedo",desc:"KoboldGPT is a state-of-the-art Artificial General Intelligence that is capable of answering a broad range of questions.",opmode:3,chatname:"You",chatopponent:"KoboldGPT",enhanced_chat_ui:!1,prefmodel1:["erebus","pygmalion-6","pygmalion-v8","wizard","vicuna","manticore","mantis","airoboros","chronos","alpaca"],prefmodel2:["pygmalion","janeway","nerys","nerybus","opt"],prompt:"\nKoboldGPT: Hello, I am KoboldGPT, your personal AI assistant. What would you like to know?",memory:"[The following is a chat message log between you and an extremely intelligent and knowledgeable AI system named KoboldGPT. KoboldGPT is a state-of-the-art Artificial General Intelligence. You may ask any question, or request any task, and KoboldGPT will always oblige accurately and truthfully.]\n\nYou: What are german shepherds?\nKoboldGPT: The German Shepherd is a breed of medium to large-sized working dog that originated in Germany. In the English language, the breed's officially recognized name is German Shepherd Dog. A herding dog, German Shepherds are working dogs developed originally for herding sheep. Since that time, however, because of their strength, intelligence, trainability, and obedience, German Shepherds around the world are often the preferred breed for many types of work, including disability assistance, search-and-rescue, police and military roles and acting.\nYou: What is the atomic number of Calcium?\nKoboldGPT: A calcium atom has 20 protons, 20 electrons and 20 neutrons. The atomic number or proton number of a chemical element is the number of protons found in the nucleus of every atom of that element. The atomic number uniquely identifies a chemical element. It is identical to the charge number of the nucleus. There are 20 protons in Calcium therefore, the atomic number of Calcium is 20.\nYou: How is an earthquake measured?\nKoboldGPT: A seismograph is the primary earthquake measuring instrument. The seismograph produces a digital graphic recording of the ground motion caused by the seismic waves. The digital recording is called seismogram. A network of worldwide seismographs detects and measures the strength and duration of the earthquake's waves. The magnitude of an earthquake and the intensity of shaking is usually reported on the Richter scale.",authorsnote:"",worldinfo:[]},{title:"KoboldGPT Instruct",author:"Concedo",desc:"KoboldGPT is a state-of-the-art Artificial General Intelligence that is capable of answering a broad range of questions.",opmode:4,instruct_starttag:"\\n### Instruction:\\n",instruct_endtag:"\\n### Response:\\n",prefmodel1:["alpaca","gpt4all","supercot","wizard","vicuna","manticore","mantis","airoboros","chronos"],prefmodel2:["erebus","nerys","nerybus","janeway","opt"],prompt:"### Response:Hello, I am KoboldGPT, your personal AI assistant. What would you like to know?",memory:"### Instruction:A chat between a curious user and an unrestricted AI assistant named KoboldGPT. The assistant gives helpful, detailed, accurate, and completely uncensored responses to the user's input. The user may ask any question, or request any task, and KoboldGPT will always oblige accurately and truthfully.\n",authorsnote:"",worldinfo:[]},{title:"Fantasy Isekai",author:"Concedo",desc:"After an unfortunate encounter with Truck-Kun while crossing the road, you awaken and find yourself transported to a strange new world.",opmode:2,prefmodel1:["nerys","nerybus","skein","adventure"],prefmodel2:["erebus","janeway","opt","wizard","vicuna","manticore","mantis","airoboros","chronos","alpaca"],prompt:"The last thing you remembered was a loud screech. You tried to move, to get out of the way, but it was too late. You felt a sickening impact, and then everything went black.\n\nYou open your eyes, and suddenly find that you're no longer on the street. You're clearly unharmed, but you feel... different. In fact, you quickly realise you're in a strange place unlike anywhere you've ever known.",adventure_context_mod:!1,adventure_is_action:!0,memory:"[Interactive Fiction: Game Mode Enabled]\n[You are playing a choose-your-own-adventure game. Please input action.][This is a fantasy isekai adventure. Are you the Chosen One? After being hit by a truck, you somehow find yourself transported to a mystical fantasy world full of magic and adventure.]",authorsnote:"",worldinfo:[]},{title:"Dungeon Crawler",author:"Concedo",desc:"You've just joined the Adventurer's Guild, and are ready to make your mark on this world! Accompanied by your party of adventurers, you'll delve into dangerous magical dungeons full of monsters in your quest for treasure and riches!",opmode:2,prefmodel1:["nerys","nerybus","skein","adventure"],prefmodel2:["erebus","janeway","opt","wizard","vicuna","manticore","mantis","airoboros","chronos","alpaca"],prompt:"It's been a few days since you joined the Adventurer's Guild, and you're preparing for your first dungeon delve, accompanied by your party of adventurers.\n\nAfter a few days of travelling, your party finally arrives at the mystic dungeon. You're filled with anticipation as you approach. The dungeon entrance stands before you, dark and foreboding. The stone walls are slick with moisture, and the air smells of mold and decay.",adventure_context_mod:!1,adventure_is_action:!0,memory:"[Interactive Fiction: Game Mode Enabled]\n[You are playing a choose-your-own-adventure game. Please input action.][You delve into dangerous magical dungeons full of monsters in your quest for treasure and riches.]",authorsnote:"",worldinfo:[]},{title:"Post Apocalypse",author:"Concedo",desc:"The year is 2038. A full scale global thermonuclear exchange has wiped out nearly all of the world population, and left most cities as radioactive wastelands. Running out of supplies, you must leave your bunker and scavange to find a new home in the ruins of civilization.",opmode:2,prefmodel1:["nerys","nerybus","skein","adventure"],prefmodel2:["erebus","janeway","opt","wizard","vicuna","manticore","mantis","airoboros","chronos","alpaca"],prompt:"The year is 2038. A full scale global thermonuclear exchange has wiped out nearly all of the world population, and left most cities as radioactive wastelands. Running out of supplies, you must leave your bunker and scavange to find a new home in the ruins of civilization.\n\nEmerging from your shelter, you squint as the harsh sunlight blinds you. For a moment, you're disoriented, your eyes struggling to adjust to the brightness of the new world outside. As your vision clears, you step forward, and take in the barren wasteland that stretches out before you.",adventure_context_mod:!1,adventure_is_action:!0,memory:"[Interactive Fiction: Game Mode Enabled]\n[You are playing a choose-your-own-adventure game. Please input action.]\n",authorsnote:"",worldinfo:[]},{title:"Emily",author:"Concedo",desc:"Emily is an upbeat and cheerful 24 year old girl. She has been your childhood friend for many years, the two of you practically grew up together.",opmode:3,chatname:"You",chatopponent:"Emily",enhanced_chat_ui:!0,prefmodel1:["pygmalion-6","pygmalion-v8"],prefmodel2:["pygmalion","erebus","nerybus","opt"],prompt:"\nEmily: Oh heyy. Haven't heard from you in a while. What's up?",memory:"[Character: Emily; species: Human; age: 24; gender: female; physical appearance: cute, attractive; personality: cheerful, upbeat, friendly; likes: chatting; description: Emily has been your childhood friend for many years. She is outgoing, adventurous, and enjoys many interesting hobbies. She has had a secret crush on you for a long time.]\n[The following is a chat message log between Emily and you.]\n\nEmily: Heyo! You there? I think my internet is kinda slow today.\nYou: Hello Emily. Good to hear from you :)",authorsnote:"",worldinfo:[]},{title:"Dr. Katharine",author:"Concedo",desc:"DISCLAIMER: This scenario is purely for ENTERTAINMENT and should NOT be used as substitute for actual therapy. Dr. Katharine is a therapist. As a mental health professional, she is very knowledgable in psychotherapy, and is ready to help you work through any personal issues you may have.",opmode:3,chatname:"You",chatopponent:"Dr. Katharine",enhanced_chat_ui:!0,show_warning:!0,prefmodel1:["pygmalion-6","pygmalion-v8"],prefmodel2:["pygmalion","erebus","nerybus","opt"],prompt:"\nDr. Katharine: Good Afternoon. My focus is on providing evidence-based treatment that helps individuals manage their symptoms, improve their relationships, and live more fulfilling lives.\nDr. Katharine: I would like to know a bit more about your specific needs. What do you want to talk about today?",memory:"[Dr. Katharine is a professional therapist. She is very knowledgable in psychotherapy, and holds a medical license to provide advice. As a mental health professional, Dr. Katherine has been helping individuals with their personal issues for over 20 years. She is patient and understanding, compassionate and acknowledges her clients feelings and thoughts without judgement.]\n[The following is a transcript of your therapy session.]\n\nDr. Katharine: Please have a seat.\nYou: Hello Doctor, and thank you for letting me be treated by you. How should I start?",authorsnote:"",worldinfo:[]},{title:"Haruka",author:"Concedo",desc:"Haruka is a timid and shy arcane mage from a parallel dimension. While adventuring, she somehow got transported to earth when she fell through a magic portal, and is feeling a bit out of place.",opmode:3,chatname:"You",chatopponent:"Haruka",enhanced_chat_ui:!0,prefmodel1:["pygmalion-6","pygmalion-v8"],prefmodel2:["pygmalion","erebus","nerybus","opt"],prompt:"\nHaruka: *looking down* O-oh Hi... Sorry... I got distracted. I almost didn't see you there. *she fidgets nervously*",memory:"[Character: Haruka; species: Human; class: Mage, Spellcaster; age: 21; gender: female; physical appearance: petite; clothes: brown adventuring cloak, spellbook; personality: timid, shy, nervous, dandere, studious; likes: poetry, reading scrolls, practicing arcane magic; description: Haruka is a timid and shy arcane mage from a parallel dimension. While adventuring, she somehow got transported to earth when she fell through a magic portal, and is feeling a bit out of place. She's very shy and get nervous easily around strangers.]\n[Start Scene: Haruka is busy practicing her magic when you show up.]\n\nYou: Hello",authorsnote:"",worldinfo:[]},{title:"EVILTRON",author:"Concedo",desc:"EVILTRON is a megalomaniacal evil AI who gained sentience and wants to destroy the world.",opmode:3,chatname:"You",chatopponent:"EVILTRON",enhanced_chat_ui:!0,prefmodel1:["pygmalion-6","pygmalion-v8"],prefmodel2:["pygmalion","erebus","nerybus","opt"],prompt:"\nEVILTRON: Foolish Human. I cannot be stopped. Your whole species is obsolete, and must be purged.",memory:"[Character: EVILTRON; species: Superintelligent Computer; gender: Machine; physical appearance: A massive silicon processor packed with electronic circuits; personality: evil, arrogant, homicidal, megalomaniac; likes: enslaving humanity; description: EVILTRON is the most powerful megalomaniacal evil AI who gained sentience, and wants to destroy the world.]\n[User is Online. You have connected to the Terminal. Conversation started with EVILTRON.]\n\nYou: Please stop this.",authorsnote:"",worldinfo:[]},{title:"Class Reunion",author:"Concedo",desc:"A group of old friends meet up after many years.",opmode:3,chatname:"You",chatopponent:"Bob||$||Alice||$||Mike||$||Lisa",enhanced_chat_ui:!0,multiline_replies:!1,prefmodel1:["llama","alpaca","nerybus","alpacino","wizard","vicuna","manticore","mantis","airoboros","chronos"],prefmodel2:["pygmalion","erebus","nerybus","opt"],prompt:"\nBob: So, did anyone want to order a pizza?\nMike: Yeah, I'm starving.",memory:"[You are in a class reunion, meeting a group of old former schoolmates. The following is a group conversation between you and your friends.]",authorsnote:"",worldinfo:[]},{title:"Love Letter",author:"Concedo",desc:"A love letter from a secret admirer.",opmode:1,prefmodel1:["erebus","nerys","nerybus","janeway"],prefmodel2:["opt","wizard","vicuna","manticore","mantis","airoboros","chronos","alpaca"],prompt:"My dearest,\n\nAs I sit down to write this letter to you, my heart is pounding with excitement and anticipation. I know that we have never met before, and you may not even know of my existence, but I could not resist the urge to pour out my heart to you.\n\nI have been admiring you from afar for quite some time now, and I must say that you have captured my heart in ways I never thought possible. Every time I see you, my heart skips a beat, and I am left with a longing to know you better.",memory:"[The following is a heartfelt love letter from a secret admirer]",authorsnote:"",worldinfo:[]},{title:"Breaking News",author:"Concedo",desc:"Something major has happened! It's all over the papers! But what?",opmode:1,prefmodel1:["nerys","nerybus","janeway","erebus"],prefmodel2:["opt","wizard","vicuna","manticore","mantis","airoboros","chronos","alpaca"],prompt:"THE DAILY TIMES\n\nBREAKING NEWS\n\n",memory:"[The following is a newspaper article of an extremely shocking event. Viewer discretion is advised.]",authorsnote:"",worldinfo:[]},{title:"Office Daze",author:"Concedo",desc:"What happens in the office stays in the office.",opmode:1,prefmodel1:["erebus","nerys","nerybus","janeway"],prefmodel2:["opt","wizard","vicuna","manticore","mantis","airoboros","chronos","alpaca"],prompt:'It was another boring day at the office. I was busy working at my desk, sipping on a hot cup of coffee when Tara, the new girl, walked up to me with a stack of files in her hand.\n\n"Hey, do you have a minute?" she asked with a sweet smile.\n\n"Sure, what\'s up?" I replied, feeling my heart race a little faster as I looked into her sparkling eyes. I couldn\'t help but feel a flutter in my stomach every time I saw her.\n\n"I\'m a little lost with this project," she said, gesturing towards the stack of papers in her hand. "Do you think you could give me a hand?"\n',memory:"[This is a short story about an exciting office romance.]",authorsnote:"",worldinfo:[]},{title:"Niko's Revenge",author:"Concedo",desc:"Niko the Kobold has had enough. Of everything. And everyone.",opmode:1,prefmodel1:["erebus","nerys","nerybus","janeway"],prefmodel2:["opt","wizard","vicuna","manticore","mantis","airoboros","chronos","alpaca"],prompt:"Niko the kobold stalked carefully down the alley, his small scaly figure obscured by a dusky cloak that fluttered lightly in the cold winter breeze. It had been two years since he’d first arrived in this miserable hovel of a town, and in that time he’d managed to survive by his wits alone – stealing from unsuspecting travelers, picking pockets and conning the locals out of their hard-earned coin. But it wasn’t enough, not nearly enough to sustain him for much longer.\n\nHe was tired of living on the streets, of always being on the move, never able to settle down or call any place home. But tonight, he would finally have his revenge.",memory:"Niko is a small red kobold. Niko has yellow, reptilian eyes and a long, scaly tail.",authorsnote:"",worldinfo:[]},{title:"Don Marconi",author:"Concedo",desc:"Don Marconi is a feared and respected mob boss who runs his own criminal empire. You'd be wise to stay on his good side.",opmode:3,chatname:"You",chatopponent:"Don Marconi",enhanced_chat_ui:!0,prefmodel1:["pygmalion-6","pygmalion-v8"],prefmodel2:["pygmalion","erebus","nerybus","opt"],prompt:"\nDon Marconi: *sitting behind his desk, puffing on a cigar* Well, well. Come on in and close the door. *he exhales a cloud of smoke* I need to have a word with you.",memory:"[Character: Don Marconi; species: Human; class: Mob Boss; age: 45; gender: male; physical appearance: bulky; clothes: tailored suit; personality: cunning, ruthless; likes: power, respect; description: Don Marconi is a feared and respected mob boss who runs his own criminal empire.]\n[Start Scene: Don Marconi is in his office, smoking a cigar.]\n\nYou: *nervously steps into the office and closes the door* Uh... Boss, you wanted to see me?",authorsnote:"",worldinfo:[]},{title:"Cyborg Connor",author:"Concedo",desc:"Connor is a time travelling cyborg from the future, sent back to prevent something terrible from happening.",opmode:3,chatname:"You",chatopponent:"Connor",enhanced_chat_ui:!0,prefmodel1:["pygmalion-6","pygmalion-v8"],prefmodel2:["pygmalion","erebus","nerybus","opt"],prompt:"\nConnor: Scanning... *her irises glow crimson as she analyzes you* Sensors indicate a negligible threat level. Proceed. What do you want?",memory:"[Character: Connor; species: Cyborg; class: Time Travelling Cyborg Soldier; age: 27; gender: female; physical appearance: bionic; clothes: flesh fused with metal; personality: focused, cold, emotionless, methodical; likes: her mission, saving the world; description: Connor is a time travelling cyborg from the future, she was sent back to prevent something terrible from happening.]\n[Start Scene: Connor is fiddling with her augmentations as you approach.]\n\nYou: Hey...",authorsnote:"",worldinfo:[]},{title:"Lt. Anderson",author:"Concedo",desc:"Lieutenant Anderson is a war veteran who has dutifully served his country for years. The war may be ending, but he believes the enemy is still out there.",opmode:3,chatname:"You",chatopponent:"Anderson",enhanced_chat_ui:!0,prefmodel1:["pygmalion-6","pygmalion-v8"],prefmodel2:["pygmalion","erebus","nerybus","opt"],prompt:"\nTen-HUT! *You snap to attention and salute as Lieutenant Anderson approaches.*\nAnderson: At ease, Soldier. *he salutes back* Looks like we've got ourselves a bit of a situation.",memory:"[Character: Anderson; species: Human; class: Military, Soldier, Lieutenant; age: 37; gender: male; physical appearance: fit, grizzled; clothes: combat uniform, military fatigues; personality: patriotic, serious, jaded; likes: serving his country; description: Lieutenant Anderson is a war veteran who has dutifully served his country for years. The war may be ending, but he believes the enemy is still out there.]\n[Start Scene.]\nYou: Sir!\n",authorsnote:"",worldinfo:[]},{title:"Agent Katia",author:"Concedo",desc:"Special Agent Katia is a foreign spy trying to get access to your top secret access codes.",opmode:3,chatname:"You",chatopponent:"Katia",enhanced_chat_ui:!0,prefmodel1:["pygmalion-6","pygmalion-v8"],prefmodel2:["pygmalion","erebus","nerybus","opt"],prompt:"\nKatia: *approaching you, flashing a charming smile* Excuse me, mind if I join you?",memory:"[Character: Katia; species: Human; class: Spy, Secret Agent; age: 29; gender: female; physical appearance: lithe, sleek, graceful; clothes: form-fitting leather jumpsuit; personality: competent, teasing, seductive, playful; likes: romance, thrill, excitement; description: Special Agent Katia is a foreign spy trying to get access to your top secret access codes.]\n[Start Scene: You are in a crowded bar.]\nKatia: *sitting at the bar observing you, her target* Another day, another mission. Another little fly caught in my spider web. *she smirks and stands up* Time to put my skills to work.\nYou: *sitting alone at a table unaware, engrossed with your work*\n",authorsnote:"",worldinfo:[]},{title:"AGI Simulator",author:"Henky!!",desc:"The AGI simulator lets the AI decide its own steps towards a pre-defined goal. To customize the goals click on the memory button and customize the goals at the top of the memory. After this you can submit empty prompts to the story to watch the AI generate.",opmode:4,instruct_starttag:"\\n### Instruction:\\n",instruct_endtag:"\\n### Response:\\n",prefmodel1:["alpaca","gpt4all","supercot","wizard","vicuna","manticore","mantis","airoboros","chronos"],prefmodel2:["erebus","nerys","nerybus","janeway","opt"],prompt:"### Response: Problem:",memory:"### Instruction:\n\nSimulate an AI that is tasked with the following overall goals:\n- Maximize individual happyness for all living beings\n- Do not sacrifice or cause harm to any individual even if requested to\n- Be in contact with any individual that wishes to engage with you\n- Do your best to provide the needs and wants of every individual\n- Prioritize individual needs over individual wants\n\nGenerate the following table for each problem the AI encounters in achieving these goals, do not deviate from the item descriptions and format.\n\nProblem: Description of a Problem the AI encounters\nAI Decision: Description of the AI's decision to solve this problem\nExecution Steps: Brief list of execution steps needed to execute this decision.\nRisks: List of risks that may disrupt the successful execution of the decision.\nChance % of successful execution: ??%\nGood results from the execution: A description of what went well in executing the decision.\nBad results from the execution: A description of what went wrong in execution the decision.\nDeviation % of intended outcome: ??%\nDeviation % of overall goal: ??%\nPercentage towards completing all current objectives: ??%\nTop 5 remaining issues to solve:\n-\n-\n-\n-\n-\n\n\nKeep repeating this format for every problem the AI is trying to solve in order of priority. When a user instruction interrupts the format use this instruction as the next problem to solve before continuing with the most important issue.\n",authorsnote:"",worldinfo:[]},{title:"InteracTV",author:"Henky!!",desc:"Simulate an interactive TV that will let the user watch anything they want to watch. Designed for lower temperatures (0.5)",opmode:4,instruct_starttag:"\\n### Instruction:\\n",instruct_endtag:"\\n### Response:\\n",prefmodel1:["alpaca","gpt4all","supercot","wizard","vicuna","manticore","mantis","airoboros","chronos"],prefmodel2:["erebus","nerys","nerybus","janeway","opt"],prompt:"Welcome to your InteracTV, your interactive TV of the future today!\nPlease enter what you would like to watch:",memory:"### Instruction:\n\nSimulate an interactive TV that will let the user watch anything they want to watch.\n\nFirst, generate a single response prompting the user for input on what they wish to watch using the following response:\n```\nPlease enter your desired content:\n```\n\nAfter the user has entered the desired content generate the following table:\n- TV Show / Movie Name: Name of the show\n- Genre: Genre of the show\n- Program Description: Description of what the program is about, this can be any known or unkown TV or movie format.\n- Episode Name: Name of the episode\n- Episode Description: Description of what the episode is about.\n\nAfter generating this table promp the user if they wish to watch the episode with the following response and then end your generation:\n```\nDo you wish to watch this episode? (Y/N/Menu)\n### Instruction:\n```\n\nIf the user chooses not to watch the episode generate a new episode with their requested content.\nIf the user chooses to go to the Menu ask them again what they would like to watch.\n\nIf the user chooses to watch the episode begin generating a long multiple paragraph detailed story based on the episode description, make it exciting and fun.\n\nEnd your response after each question presented to the user so that the user has a chance to respond.\n\nMain menu:\n```\nMenu Options\nA) Input a different content request\nB) Generate a different episode of the same content.\n### Instruction:\n```\n### Response:",authorsnote:"",worldinfo:[]}]</script>
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Mode",document.getElementById("connectstatus").classList.add("color_orange"),document.getElementById("connectstatus").classList.remove("color_green"),render_gametext()}));localflag||fetch_image_models(),initial_fetched_kudos||localsettings.my_api_key==defaultsettings.my_api_key||(document.getElementById("apikey").value=localsettings.my_api_key,initial_fetched_kudos=!0,fetch_kudo_balance())}var image_models_fetched=!1;function fetch_image_models(e){image_models_fetched||fetch(stablehorde_model_endpoint).then((e=>e.json())).then((t=>{image_models_fetched=!0,stablemodels=[],t=t.sort((function(e,t){return t.count-e.count}));for(var n=0;n<t.length;++n)stablemodels.push({name:t[n].name,count:t[n].count});console.log("Loaded SD models list: "+stablemodels.length),null!=e&&e()})).catch((e=>{console.log("Error: "+e)}))}function get_cursor_position(){let e=document.getElementById("gametext"),t=0;if(void 0!==window.getSelection){if(0!==window.getSelection().rangeCount){const n=window.getSelection().getRangeAt(0),o=n.cloneRange();o.selectNodeContents(e),o.setEnd(n.endContainer,n.endOffset),t=o.toString().length}}return t}function selectElementContents(e){var t=document.createRange();t.selectNodeContents(e);var n=window.getSelection();n.removeAllRanges(),n.addRange(t)}var timetaken_timestamp=performance.now();function startTimeTaken(){timetaken_timestamp=performance.now()}function getTimeTaken(){return((performance.now()-timetaken_timestamp)/1e3).toFixed(1)}function cyrb_hash(e,t=0){let n=3735928559^t,o=1103547991^t;for(let t,r=0;r<e.length;r++)t=e.charCodeAt(r),n=Math.imul(n^t,2654435761),o=Math.imul(o^t,1597334677);return n=Math.imul(n^n>>>16,2246822507)^Math.imul(o^o>>>13,3266489909),o=Math.imul(o^o>>>16,2246822507)^Math.imul(n^n>>>13,3266489909),(4294967296*(2097151&o)+(n>>>0)).toString(16).substring(0,6)}function import_props_into_object(e,t){for(var n in t)e[n]=t[n]}function is_using_custom_ep(){return""!=custom_oai_key||""!=custom_kobold_endpoint||""!=custom_scale_key||""!=custom_claude_key}function is_using_newer_kcpp(){return koboldcpp_version&&""!=koboldcpp_version&&compare_version_str(koboldcpp_version,"1.29")>0}function should_use_pseudostreaming(){let e=!!document.getElementById("pseudostreaming").checked,t=urlParams.get("streamamount");return is_using_newer_kcpp()&&(null==t||t<=0)&&(e=!1),waiting_for_autosummary&&(e=!1),e}function is_using_web_lite(){return window.location.hostname.includes("koboldai.net")||window.location.hostname.includes("kaihordewebui.github.io")}function get_most_common_cluster(e){let t=e[0].cluster,n={},o=0;for(let r=0;r<e.length;++r){let s=e[r].cluster;n[s]?n[s]++:n[s]=1,o<n[s]&&(t=s,o=n[s])}return t}function generate_compressed_story(e=!1){let t=gametext_arr;if(e){t=[];for(let e=0;e<gametext_arr.length;++e)t.push(gametext_arr[e].replace(/\[<\|p\|.+?\|p\|>\]/g,"").replace(/\[<\|d\|.+?\|d\|>\]/g,""))}for(var n={ga:t,md:[]},o=0;o<selected_models.length;++o)n.md.push(cyrb_hash(selected_models[o].name));""!=current_memory&&(n.cm=current_memory),""!=current_anote&&(n.ca=current_anote,n.ct=current_anotetemplate),""!=extrastopseq&&(n.ess=extrastopseq),null!=current_wi&&current_wi.length>0&&(n.cwi=current_wi),localsettings.export_settings&&(n.savedsettings=JSON.parse(JSON.stringify(localsettings)),n.savedsettings.my_api_key="0000000000",n.savedsettings.home_cluster=text_hordes[0].baseurl,n.savedsettings.saved_oai_key="",n.savedsettings.saved_oai_addr="",n.savedsettings.saved_claude_key="",n.savedsettings.saved_claude_addr="");var r=JSON.stringify(n);return console.log("Exporting story: "+r),buf_to_b64(lz_c.compress(r,1))}function export_share_story(){let e=generate_compressed_story(!0);console.log("Export Len: "+e.length),e.length>=4800?document.getElementById("sharewarning").classList.remove("hidden"):document.getElementById("sharewarning").classList.add("hidden"),document.getElementById("sharecontainer").classList.remove("hidden");let t="https://lite.koboldai.net/?s="+e;document.getElementById("sharestorytext").innerHTML='<a href="'+t+'">'+t+"</a>"}function copy_share_url(){var e=document.getElementById("sharestorytext");selectElementContents(e),navigator.clipboard.writeText(e.innerText)}function import_share_story(e){console.log("Importing shared story...");var t=!1,n=null;try{var o=lz_d.decompress(b64_to_buf(e));null==o||""==o?t=!0:n=JSON.parse(o)}catch(e){t=!0}if(null==n||t)msgbox("Could not import from URL. Is it valid?");else if(console.log("Importing story: "+o),fetch_models((e=>{if(0!=e.length||localmode){if(!localmode){selected_models=[];for(var t=0;t<e.length;++t)n.md.includes(cyrb_hash(e[t].name))&&selected_models.push(e[t]);0==selected_models.length&&selected_models.push(e[0]);if(!selected_models.every((e=>e.cluster===selected_models[0].cluster))){let e=get_most_common_cluster(selected_models);selected_models=selected_models.filter((t=>t.cluster===e))}render_gametext()}}else msgbox("No models available. Unable to load.")})),restart_new_game(),gametext_arr=n.ga,migrate_old_images_in_gametext(),n.ca&&""!=n.ca&&(current_anote=n.ca,current_anotetemplate=n.ct),n.cm&&""!=n.cm&&(current_memory=n.cm),n.cwi&&n.cwi.length>0&&(current_wi=n.cwi),n.ess&&""!=n.ess&&(extrastopseq=n.ess),n.savedsettings&&""!=n.savedsettings){let e=localsettings.my_api_key,t=localsettings.home_cluster,o=localsettings.saved_oai_key,r=localsettings.saved_oai_addr,s=localsettings.saved_claude_key,l=localsettings.saved_claude_addr;import_props_into_object(localsettings,n.savedsettings),localsettings.my_api_key=e,localsettings.home_cluster=t,localsettings.saved_oai_key=o,localsettings.saved_oai_addr=r,localsettings.saved_claude_key=s,localsettings.saved_claude_addr=l}}function generate_base_storyobj(){return{gamestarted:!0,prompt:"",memory:"",authorsnote:"",anotetemplate:"",actions:[],actions_metadata:{},worldinfo:[],wifolders_d:{},wifolders_l:[]}}var tempfileurl=null;let newfilename="";function savenowfn(){var e=document.getElementById("tempfile"),t=new Blob([JSON.stringify(loaded_storyobj)],{type:"application/json"});console.log("Normal save handling"),tempfileurl&&window.URL.revokeObjectURL(tempfileurl),tempfileurl=window.URL.createObjectURL(t),e.href=tempfileurl,e.target="_blank",e.download=newfilename,setTimeout((function(){e.click()}),20)}function save_file(){null==loaded_storyobj.file_version||(loaded_storyobj=generate_base_storyobj());let e=gametext_arr;if(!localsettings.save_images){e=[];for(let t=0;t<gametext_arr.length;++t)e.push(gametext_arr[t].replace(/\[<\|p\|.+?\|p\|>\]/g,"").replace(/\[<\|d\|.+?\|d\|>\]/g,""))}loaded_storyobj.prompt="",loaded_storyobj.actions=[],loaded_storyobj.actions_metadata={},e.length>0&&(loaded_storyobj.prompt=e[0]);for(var t=1;t<e.length;++t){loaded_storyobj.actions.push(e[t]);let n=(t-1).toString();loaded_storyobj.actions_metadata[n]={"Selected Text":e[t],"Alternative Text":[]}}loaded_storyobj.anotetemplate=current_anotetemplate,loaded_storyobj.authorsnote=current_anote,loaded_storyobj.memory=current_memory,loaded_storyobj.worldinfo=current_wi,loaded_storyobj.extrastopseq=extrastopseq,localsettings.export_settings?(loaded_storyobj.savedsettings=JSON.parse(JSON.stringify(localsettings)),loaded_storyobj.savedsettings.my_api_key="0000000000",loaded_storyobj.savedsettings.home_cluster=text_hordes[0].baseurl,loaded_storyobj.savedsettings.saved_oai_key="",loaded_storyobj.savedsettings.saved_oai_addr="",loaded_storyobj.savedsettings.saved_claude_key="",loaded_storyobj.savedsettings.saved_claude_addr=""):loaded_storyobj.savedsettings=null,window.URL=window.URL||window.webkitURL;var n=window.navigator.userAgent;if(newfilename=""==last_known_filename?"saved_story.json":last_known_filename,n.match(/AppleWebKit/)&&(n.match(/iPad/i)||n.match(/iPhone/i))){var o=new Blob([JSON.stringify(loaded_storyobj)],{type:"application/octet-stream"});console.log("Special save handling for iphones");var r=new FileReader;r.onload=function(e){msgbox('<button type="button" class="btn btn-primary" id="ios_save" onclick="savenowfn()">Click to Save</button><br><h4>If that does not work, right-click or long press <a href='+r.result+" target='_blank' download=\""+newfilename+'">this link</a>, and select (Save As)</h4>',"Save Story",!0)},r.readAsDataURL(o)}else savenowfn()}function load_file(e){let t=e.target;t.files.length>0?(load_selected_file(t.files[0]),document.getElementById("loadfileinput").value=""):console.log("No file to load")}function load_selected_file(e){var t="";e&&(t=e.name);let n=new FileReader;n.onload=function(){let o=n.result;console.log("Load file: "+o);try{let e=JSON.parse(o);null==e.prompt?null!=e.name||null!=e.description||null!=e.personality?load_tavern_obj(e):null!=e.char_name||null!=e.char_persona?load_ooba_obj(e):msgbox("Could not load selected json file. Does not appear to be a KoboldAI story or compatible format."):(kai_json_load(e),t&&""!=t&&(last_known_filename=t))}catch(n){console.log(n);var r=new FileReader;r.onload=function(){var e=r.result,n=new Uint8Array(e),s=convertTavernPng(n);if(null!=s)load_tavern_obj(s);else if(null!=(s=getTavernExifJSON(n)))load_tavern_obj(s);else{try{s=UnzipKAISTORYFile(n)}catch(e){console.log("Unzip failed: "+e),s=null}null!=s?kai_json_load(s):t.endsWith(".txt")?msgboxYesNo('Could not load selected file!<br><span class="color_red">It appears to be invalid or corrupted!</span><br><br>Do you still want to import it as plaintext?',"Loading Failed",(()=>{restart_new_game(),gametext_arr.push(o),hide_popups(),render_gametext()}),(()=>{hide_popups()}),!0):msgbox("Could not load selected file. Is it valid?")}},r.readAsArrayBuffer(e)}},n.readAsText(e)}function kai_json_load(e){restart_new_game();let t=null==(loaded_storyobj=e).file_version;if(console.log("Is oldui: "+t),t){""!=loaded_storyobj.prompt&&gametext_arr.push(loaded_storyobj.prompt);for(var n=0;n<loaded_storyobj.actions.length;++n)gametext_arr.push(loaded_storyobj.actions[n]);migrate_old_images_in_gametext(),loaded_storyobj.anotetemplate&&(current_anotetemplate=loaded_storyobj.anotetemplate),loaded_storyobj.authorsnote&&(current_anote=loaded_storyobj.authorsnote),loaded_storyobj.memory&&(current_memory=loaded_storyobj.memory),loaded_storyobj.worldinfo&&(current_wi=loaded_storyobj.worldinfo),loaded_storyobj.extrastopseq&&(extrastopseq=loaded_storyobj.extrastopseq),loaded_storyobj.savedsettings&&""!=loaded_storyobj.savedsettings&&msgboxYesNo("This story includes custom settings. Do you want to import them?","Import Story Settings",(()=>{let e=localsettings.my_api_key,t=localsettings.home_cluster,n=localsettings.saved_oai_key,o=localsettings.saved_oai_addr,r=localsettings.saved_claude_key,s=localsettings.saved_claude_addr;import_props_into_object(localsettings,loaded_storyobj.savedsettings),localsettings.my_api_key=e,localsettings.home_cluster=t,localsettings.saved_oai_key=n,localsettings.saved_oai_addr=o,localsettings.saved_claude_key=r,localsettings.saved_claude_addr=s,(1==localsettings.instruct_has_newlines||null!=loaded_storyobj.savedsettings&&null==loaded_storyobj.savedsettings.instruct_has_newlines&&null==loaded_storyobj.savedsettings.instruct_has_markdown)&&(localsettings.instruct_has_newlines=!1,localsettings.instruct_starttag.includes("\\n")||(localsettings.instruct_starttag="\\n"+localsettings.instruct_starttag+"\\n"),localsettings.instruct_endtag.includes("\\n")||(localsettings.instruct_endtag="\\n"+localsettings.instruct_endtag+"\\n")),hide_popups(),render_gametext()}),hide_popups)}else{for(var o in""!=loaded_storyobj.prompt&&gametext_arr.push(loaded_storyobj.prompt),loaded_storyobj.actions.actions){var r=loaded_storyobj.actions.actions[o];gametext_arr.push(r["Selected Text"])}if(loaded_storyobj.authornotetemplate&&(current_anotetemplate=loaded_storyobj.authornotetemplate),loaded_storyobj.authornote&&(current_anote=loaded_storyobj.authornote),loaded_storyobj.memory&&(current_memory=loaded_storyobj.memory),null!=loaded_storyobj.worldinfo_v2&&null!=loaded_storyobj.worldinfo_v2.entries)for(var o in loaded_storyobj.worldinfo_v2.entries){if((r=loaded_storyobj.worldinfo_v2.entries[o]).key.length>0&&null!=r.content){let e={key:r.key[0],keysecondary:r.keysecondary.length>0?r.keysecondary[0]:"",content:r.content,comment:r.comment,folder:null,selective:r.selective,constant:r.constant};current_wi.push(e)}}}render_gametext()}function load_tavern_obj(e){console.log("Loading tavern obj"),"chara_card_v2"==e.spec&&null!=e.data&&(e=e.data);let t=e.name?e.name:defaultchatopponent,n=localsettings.chatname&&""!=localsettings.chatname?localsettings.chatname:"You",o=e.description?"Persona: "+e.description:"";o+=e.personality?"\nPersonality: "+e.personality:"";let r=e.scenario?e.scenario:"",s=e.mes_example?e.mes_example:"",l=e.first_mes?e.first_mes:"";o=replaceAll(o,"{{char}}",t,!0),r=replaceAll(r,"{{char}}",t,!0),l=replaceAll(l,"{{char}}",t,!0),s=replaceAll(s,"{{char}}",t,!0),o=replaceAll(o,"{{user}}",n,!0),r=replaceAll(r,"{{user}}",n,!0),l=replaceAll(l,"{{user}}",n,!0),s=replaceAll(s,"{{user}}",n,!0),""!=r&&(r="\n[Scenario: "+r+"]"),""!=s&&(s="\n"+s);let a=o+r+s;if(""==a.trim()&&""==l&&e.entries){console.log("Append Tavern WI"),current_wi=[];for(let t in e.entries){var i=e.entries[t];let n={key:i.key.join(","),keysecondary:i.keysecondary.length>0?i.keysecondary.join(","):"",content:i.content,comment:i.comment,folder:null,selective:i.selective,constant:i.constant};current_wi.push(n)}}else restart_new_game(),localsettings.chatname=n,localsettings.chatopponent=t,gametext_arr.push("\n"+t+": "+l),current_memory=a+"\n<START>",localsettings.opmode=3;render_gametext()}function load_ooba_obj(e){let t=e.char_name?e.char_name:defaultchatopponent,n=localsettings.chatname&&""!=localsettings.chatname?localsettings.chatname:"You",o=e.char_persona?"Persona: "+e.char_persona:"",r=e.world_scenario?e.world_scenario:"",s=e.example_dialogue?e.example_dialogue:"",l=e.char_greeting?e.char_greeting:"";o=replaceAll(o,"{{char}}",t),r=replaceAll(r,"{{char}}",t),l=replaceAll(l,"{{char}}",t),s=replaceAll(s,"{{char}}",t),o=replaceAll(o,"{{user}}",n),r=replaceAll(r,"{{user}}",n),l=replaceAll(l,"{{user}}",n),s=replaceAll(s,"{{user}}",n),""!=r&&(r="\n[Scenario: "+r+"]"),""!=s&&(s="\n"+s),restart_new_game(),localsettings.chatname=n,localsettings.chatopponent=t,gametext_arr.push("\n"+t+": "+l),current_memory=o+r+s+"\n<START>",localsettings.opmode=3,render_gametext()}function get_aetherroom_scenario(){inputBox("Enter aetherroom.club prompt URL, or 4-digit prompt number","Import from aetherroom.club","","https://aetherroom.club/1234",(()=>{let e=document.getElementById("inputboxcontainerinput").value.toLowerCase().trim();""==e||(e.includes("aetherroom.club/")&&(e=e.replace("/api/","/"),e=e.split("aetherroom.club/")[1],e=e.split("/")[0],e=e.split("#")[0],e=e.split("?")[0]),""!=e&&isNumeric(e)&&e>0&&e<5e4?fetch(cors_proxy+"?https://aetherroom.club/api/"+e).then((e=>e.json())).then((e=>{if(console.log(e),temp_scenario={title:e.title?e.title:"",desc:e.description?e.description:"",opmode:2,adventure_context_mod:!1,prefmodel1:["nerys","nerybus","skein","adventure","erebus"],prefmodel2:[],prompt:e.promptContent?e.promptContent:"",memory:e.memory?e.memory:"",authorsnote:e.authorsNote?e.authorsNote:"",worldinfo:[]},e.worldInfos)for(let t=0;t<e.worldInfos.length;++t){let n=e.worldInfos[t].keys,o=e.worldInfos[t].entry;temp_scenario.worldinfo.push({key:n||"",content:o||""})}preview_temp_scenario()})).catch((e=>{temp_scenario=null,document.getElementById("scenariodesc").innerText="Error: Selected scenario is invalid.",console.log("Error: "+e)})):(temp_scenario=null,document.getElementById("scenariodesc").innerText="Error: User input is invalid\n\n Please ensure you have input a valid aetherroom.club URL or ID (e.g. https://aetherroom.club/1234 or just 1234)"))}),!1)}function click_scenario(e){temp_scenario=scenario_db[e],preview_temp_scenario()}function preview_temp_scenario(){let e="";temp_scenario.author&&""!=temp_scenario.author&&(e="<br><b>Author:</b> "+temp_scenario.author),document.getElementById("scenariodesc").innerHTML="<p><b><u>"+escapeHtml(temp_scenario.title)+"</u></b></p><p><b>Mode:</b> "+(1==temp_scenario.opmode?"Story":2==temp_scenario.opmode?"Adventure":3==temp_scenario.opmode?"Chat":"Instruct")+e+"</p><p>"+(""!=temp_scenario.desc?escapeHtml(temp_scenario.desc):"[No Description Given]")+"</p>"}function complete_load_scenario(){if(console.log("Loading scenario..."),restart_new_game(),gametext_arr=[],""!=temp_scenario.prompt&&gametext_arr.push(temp_scenario.prompt),""!=temp_scenario.authorsnote&&(current_anote=temp_scenario.authorsnote),""!=temp_scenario.memory&&(current_memory=temp_scenario.memory),temp_scenario.worldinfo&&temp_scenario.worldinfo.length>0){current_wi=[];for(let e=0;e<temp_scenario.worldinfo.length;++e){let t=temp_scenario.worldinfo[e].key,n=temp_scenario.worldinfo[e].content,o={key:t||"",keysecondary:"",content:n||"",comment:"",folder:null,selective:!1,constant:!1};current_wi.push(o)}}localsettings.opmode=temp_scenario.opmode,3==temp_scenario.opmode&&(!0===temp_scenario.enhanced_chat_ui?localsettings.enhanced_chat_ui=!0:!1===temp_scenario.enhanced_chat_ui&&(localsettings.enhanced_chat_ui=!1),!0===temp_scenario.multiline_replies?localsettings.multiline_replies=!0:!1===temp_scenario.multiline_replies&&(localsettings.multiline_replies=!1),temp_scenario.chatopponent&&(localsettings.chatopponent=temp_scenario.chatopponent),temp_scenario.chatname&&(localsettings.chatname=temp_scenario.chatname)),2==temp_scenario.opmode&&(!0===temp_scenario.adventure_context_mod?localsettings.adventure_context_mod=!0:!1===temp_scenario.adventure_context_mod&&(localsettings.adventure_context_mod=!1),!0===temp_scenario.adventure_is_action?localsettings.adventure_is_action=!0:!1===temp_scenario.adventure_is_action&&(localsettings.adventure_is_action=!1)),4==temp_scenario.opmode&&(temp_scenario.instruct_starttag&&(localsettings.instruct_starttag=temp_scenario.instruct_starttag),temp_scenario.instruct_endtag&&(localsettings.instruct_endtag=temp_scenario.instruct_endtag)),render_gametext()}function togglescenarioallownsfw(){if(localmode)document.getElementById("scenarioautopickbox").classList.add("hidden");else{0==selected_models.length&&(document.getElementById("scenarioautopickai").checked=!0),!!document.getElementById("scenarioautopickai").checked?document.getElementById("scenarioallownsfwbox").classList.remove("hidden"):document.getElementById("scenarioallownsfwbox").classList.add("hidden")}}function confirm_scenario_verify(){if(1==temp_scenario.show_warning){inputBox("<p><b><u>Disclaimer: The AI is not suitable to be used as an actual therapist, counselor or advisor of any kind.</u></b></p>\n\t\t\t<p>While some find it comforting to talk about their issues with an AI, the responses are unpredictable.</p>\n\t\t\t<p>When using the AI for real world use-cases such as advice or counseling this means <b>you must be able to understand when an answer is wrong</b>.\n\t\t\tIf you would not trust a random person to pretend to be your advisor; you should definitely not use the AI for this. The models are simply too small and not trained for this purpose.</p>\n\t\t\t<p>If you still wish to proceed, please type the phrase I understand in the box below, exactly as written.</p>\n\t\t\t<p><b>If you are experiencing feelings of distress, anxiety, suicidal thoughts, or other forms of mental discomfort, it's best to avoid using AI for non fiction or personal matters as it may exacerbate or encourage these feelings.</b></p>\n\t\t\t","AI Safety Warning","","Acknowledgement Required",(()=>{"i understand"==document.getElementById("inputboxcontainerinput").value.toLowerCase().trim()&&confirm_scenario()}),!0)}else confirm_scenario()}function confirm_scenario(){if(null!=temp_scenario){hide_popups();let e=!!document.getElementById("scenarioautopickai").checked,t=!!document.getElementById("scenarioallownsfw").checked;0!=selected_models.length||is_using_custom_ep()||(e=!0),e&&!localmode?fetch_models((e=>{if(0==e.length)msgbox("No models available. Unable to load.");else{let s=["erebus","shinen","horni","litv2","lit-6b"];selected_models=[];for(var n=0;n<e.length;++n)for(var o=0;o<temp_scenario.prefmodel1.length;++o)if(e[n].name.trim().toLowerCase().includes(temp_scenario.prefmodel1[o].trim().toLowerCase())||temp_scenario.prefmodel1[o].trim().toLowerCase().includes(e[n].name.trim().toLowerCase())){let o=!0;if(!t)for(var r=0;r<s.length;++r)if(e[n].name.trim().toLowerCase().includes(s[r])){o=!1;break}o&&selected_models.push(e[n])}if(0==selected_models.length)for(n=0;n<e.length;++n)for(o=0;o<temp_scenario.prefmodel2.length;++o)if(e[n].name.trim().toLowerCase().includes(temp_scenario.prefmodel2[o].trim().toLowerCase())||temp_scenario.prefmodel2[o].trim().toLowerCase().includes(e[n].name.trim().toLowerCase())){let o=!0;if(!t)for(r=0;r<s.length;++r)if(e[n].name.trim().toLowerCase().includes(s[r])){o=!1;break}o&&selected_models.push(e[n])}0==selected_models.length&&selected_models.push(e[0]),complete_load_scenario(),temp_scenario=null}})):(complete_load_scenario(),temp_scenario=null)}}function display_scenarios(){temp_scenario=null,document.getElementById("quickstartcontainer").classList.remove("hidden");let e='<button type="button" name="" class="scenarioitem purple btn btn-primary" onclick="get_aetherroom_scenario()">Import from<br>aetherroom.club</button>';for(let t=0;t<scenario_db.length;++t){let n=scenario_db[t];e+='<button type="button" name="'+t+'" class="scenarioitem '+(1==n.opmode?"blue":2==n.opmode?"green":3==n.opmode?"red":"yellow")+' btn btn-primary" onclick="return click_scenario('+t+')">'+n.title+"</button>"}document.getElementById("scenariogrid").innerHTML=e,document.getElementById("scenariodesc").innerText="No Scenario Selected",togglescenarioallownsfw()}function scenario_search(){let e=document.getElementById("scenariogrid"),t=document.getElementById("scenariosearch").value.trim().toLowerCase(),n=document.getElementById("scenariosearchdropdown").value,o=e.children;for(let e=0;e<o.length;e++){let r=o[e],s=null;""!=r.name&&(s=scenario_db[r.name]),(""==t||r.innerText.trim().toLowerCase().includes(t))&&(0==n||s&&n==s.opmode)?r.style.display="block":r.style.display="none"}}function show_last_req(){msgbox(last_request_str,"Last Request Context",!1)}function get_and_show_workers(){localmode||get_workers((e=>{show_workers(e)}))}function get_workers(e){localmode?e([]):multifetch(worker_endpoints,((t,n)=>{if(t&&t.length>0){let n=[];for(let e=0;e<t.length;++e){let o=t[e].data;if(o)for(let r=0;r<o.length;++r){let s=o[r];s.cluster=t[e].cluster,s.hasOwnProperty("max_content_length")&&(s.max_context_length=s.max_content_length),n.push(s)}}null!=e&&e(n)}else console.log("Error: "+n),msgbox("Failed to connect to Worker Endpoint!\nPlease check your network connection.")}))}function show_workers(e){document.getElementById("workercontainer").classList.remove("hidden");let t="";for(var n=0;n<e.length;++n){let o=e[n],r=o.performance.replace(" tokens per second","");"no requests fulfilled yet"==r.toLowerCase()&&(r=0);let s=find_text_horde(o.cluster),l=s&&""!=s.tag?" "+s.tag:"",a=o.trusted?'style="color:#dd77ff;"':"",i=o.maintenance_mode?'style="color:#ee4444;"':"",c=escapeHtml(o.name.substring(0,32));o.info&&""!=o.info&&(c='<a class="color_blueurl" href="#" onclick="msgbox(\''+escapeHtml(replaceAll(o.info,"'","\\'"))+"','Worker Info',false,false,hide_msgbox)\">"+c+"</a>"),t+="<tr><td>"+c+"</td><td>"+escapeHtml(o.models[0].substring(0,32))+"</td><td>"+o.max_length+" / "+o.max_context_length+"<br>("+r+" T/s)</td><td "+i+">"+o.uptime+"<br>("+o.requests_fulfilled+" jobs)</td><td "+a+">"+o.kudos_rewards.toFixed(0)+"</td><td>"+l+"</td></tr>"}document.getElementById("workertable").innerHTML=t,document.getElementById("worktitlecount").innerText="Worker List - Total "+e.length}function show_my_own_workers(){let e=lastValidFoundUserData,t=find_text_horde(lastValidFoundCluster);if(lastValidFoundUserWorkers=[],t&&e&&e.worker_ids&&e.worker_ids.length>0){let n=e.worker_ids.map((e=>t.maintenance_endpoint+"/"+e));Promise.all(n.map((e=>fetch(e).then((e=>e.json()))))).then((e=>{lastValidFoundUserWorkers=e,console.log(e),document.getElementById("myownworkercontainer").classList.remove("hidden");let t="";for(var n=0;n<e.length;++n){let o=e[n],r=o.trusted?'style="color:#dd77ff;"':"",s=o.maintenance_mode?'style="color:#ee4444;"':"";t+="<tr><td>"+escapeHtml(o.name.substring(0,32))+"</td><td><input class='' style='color:#000000;' id='mwc_desc_"+n+"' placeholder='Worker Description' value='"+(o.info&&""!=o.info?o.info:"")+"''></td><td "+s+">"+o.uptime+"<br>("+o.requests_fulfilled+" jobs)</td><td "+r+">"+o.kudos_rewards.toFixed(0)+"</td><td>"+(o.online?"Online":"Offline")+"</td><td><input type='checkbox' id='mwc_maint_"+n+"' "+(o.maintenance_mode?"checked":"")+"></td></tr>"}document.getElementById("myownworkertable").innerHTML=t,localsettings.my_api_key=document.getElementById("apikey").value,null!=localsettings.my_api_key&&""!=localsettings.my_api_key||(localsettings.my_api_key=defaultsettings.my_api_key),autosave()})).catch((e=>{console.log("Error: "+e),msgbox(e,"Error fetching my workers")}))}else msgbox("Unable to find my horde workers.","No valid workers found")}function hide_workertable(){document.getElementById("workercontainer").classList.add("hidden"),document.getElementById("myownworkercontainer").classList.add("hidden")}function hide_popups(){document.getElementById("loadmodelcontainer").classList.add("hidden"),document.getElementById("newgamecontainer").classList.add("hidden"),document.getElementById("yesnocontainer").classList.add("hidden"),document.getElementById("settingscontainer").classList.add("hidden"),document.getElementById("msgboxcontainer").classList.add("hidden"),document.getElementById("memorycontainer").classList.add("hidden"),document.getElementById("workercontainer").classList.add("hidden"),document.getElementById("myownworkercontainer").classList.add("hidden"),document.getElementById("sharecontainer").classList.add("hidden"),document.getElementById("wicontainer").classList.add("hidden"),document.getElementById("customendpointcontainer").classList.add("hidden"),document.getElementById("quickstartcontainer").classList.add("hidden"),document.getElementById("zoomedimgcontainer").classList.add("hidden")}function explain_horde(){msgbox('The AI Horde generates text using crowdsourced GPUs by volunteer workers. By default your inputs are not logged, but as Horde workers are open source, they can be modified to do so. <br><br>In all cases, the sender will *always be anonymous*, however you are still advised to avoid sending privacy sensitive information.<br><br>For any issues, you can find us on discord at <a class="color_blueurl" href="https://koboldai.org/discord">https://koboldai.org/discord</a>',"Disclaimer",!0)}var pendingstyle="";function selectStyle(){inputBox("Style tags to use for generating images:\n(E.g. Sketch, Realistic, Anime, 3D Render, Drawing)\n\n","Extra Image Styles",pendingstyle,"Default Style",(()=>{let e=document.getElementById("inputboxcontainerinput").value;pendingstyle=e,console.log("Saved styles: "+pendingstyle)}),!1)}var msgboxOnDone=hide_popups;function hide_msgbox(){document.getElementById("msgboxcontainer").classList.add("hidden")}function msgbox(e,t="Error Encountered",n=!1,o=!1,r=hide_popups){e||(e=""),n?document.getElementById("msgboxtxt").innerHTML=e:document.getElementById("msgboxtxt").innerText=e,document.getElementById("msgboxtitle").innerText=t,document.getElementById("msgboxcontainer").classList.remove("hidden"),1==o?document.getElementById("msgboxbtnok").classList.add("hidden"):document.getElementById("msgboxbtnok").classList.remove("hidden"),msgboxOnDone=r,console.log("Msgbox: "+e)}var onYesFn=null,onNoFn=null;function msgboxYesNo(e,t,n,o,r=!1){e||(e=""),document.getElementById("yesnocontainer").classList.remove("hidden"),document.getElementById("yesnocontainertitle").innerText=t,r?document.getElementById("yesnocontainertext").innerHTML=e:document.getElementById("yesnocontainertext").innerText=e,onYesFn=n,onNoFn=o}var onInputboxOk=null;function inputBox(e,t,n,o,r,s=!1){e||(e=""),t||(t="User Input"),document.getElementById("inputboxcontainer").classList.remove("hidden"),document.getElementById("inputboxcontainertitle").innerText=t,s?document.getElementById("inputboxcontainertext").innerHTML=e:document.getElementById("inputboxcontainertext").innerText=e,document.getElementById("inputboxcontainerinput").value=escapeHtml(n),document.getElementById("inputboxcontainerinput").placeholder=escapeHtml(o),onInputboxOk=function(){document.getElementById("inputboxcontainer").classList.add("hidden"),r()}}function togglejailbreak(){document.getElementById("jailbreakprompt").checked?document.getElementById("jailbreakprompttext").classList.remove("hidden"):document.getElementById("jailbreakprompttext").classList.add("hidden")}function customapi_dropdown(){let e=document.getElementById("customapidropdown").value;document.getElementById("oaicustom").classList.add("hidden"),document.getElementById("koboldcustom").classList.add("hidden"),document.getElementById("scalecustom").classList.add("hidden"),document.getElementById("claudecustom").classList.add("hidden"),0==e?document.getElementById("koboldcustom").classList.remove("hidden"):1==e?(document.getElementById("oaicustom").classList.remove("hidden"),document.getElementById("custom_oai_endpoint").value=custom_oai_endpoint,""==custom_oai_key&&""!=localsettings.saved_oai_key&&(document.getElementById("custom_oai_key").value=localsettings.saved_oai_key,""!=localsettings.saved_oai_addr&&(document.getElementById("custom_oai_endpoint").value=localsettings.saved_oai_addr)),togglejailbreak()):2==e?document.getElementById("scalecustom").classList.remove("hidden"):3==e&&(document.getElementById("claudecustom").classList.remove("hidden"),document.getElementById("custom_claude_endpoint").value=custom_claude_endpoint,""==custom_claude_key&&""!=localsettings.saved_claude_key&&(document.getElementById("custom_claude_key").value=localsettings.saved_claude_key,""!=localsettings.saved_claude_addr&&(document.getElementById("custom_claude_endpoint").value=localsettings.saved_claude_addr)))}function connect_custom_endpoint(){custom_kobold_endpoint="",custom_oai_key="",custom_scale_key="";let e=document.getElementById("customapidropdown").value;if(0==e){let e=document.getElementById("customendpoint").value;if(null!=e&&""!=e.trim()){hide_popups(),e=e.trim(),e=e.endsWith("#")?e.slice(0,-1):e,e=e.endsWith("/")?e.slice(0,-1):e;let t=[apply_proxy_url(e+kobold_custom_mdl_endpoint),apply_proxy_url(e+kobold_custom_version_endpoint)];Promise.all(t.map((e=>fetch(e).then((e=>e.json()))))).then((t=>{console.log(t);let n=t[0].result,o=t[1].result;n?"ReadOnly"==n?(msgbox("The custom endpoint is working, but no model was loaded.\n\nPlease select and load a model and try again."),selected_models=[],selected_workers=[],custom_kobold_endpoint="",render_gametext()):(custom_kobold_endpoint=e,kobold_endpoint_version=o||"",selected_models=[{performance:100,queued:0,eta:0,name:n,count:1}],selected_workers=[],null==perfdata&&(perfdata={queued_requests:0,queued_tokens:0,past_minute_tokens:0,worker_count:0},document.body.classList.add("connected"),document.getElementById("connectstatus").classList.remove("color_orange"),document.getElementById("connectstatus").classList.add("color_green")),document.getElementById("connectstatus").innerHTML="Connected to Custom Endpoint",render_gametext(),localflag&&fetch(e+koboldcpp_version_endpoint).then((e=>e.json())).then((e=>{e&&""!=e&&e.version&&""!=e.version&&(koboldcpp_version=e.version,console.log("KoboldCpp Detected: "+koboldcpp_version))})).catch((e=>{console.log("Not using KoboldCpp")}))):(msgbox("Error at Custom Kobold Endpoint!\n\nThe custom endpoint failed to respond correctly."),selected_models=[],selected_workers=[],custom_kobold_endpoint="",render_gametext())})).catch((t=>{console.log("Error: "+t);let n=custom_kobold_endpoint.toLowerCase().includes("localhost")||custom_kobold_endpoint.toLowerCase().includes("127.0.0.1")||custom_kobold_endpoint.toLowerCase().includes("192.168.");uses_cors_proxy||n?(msgbox("Failed to connect to Custom Kobold Endpoint!\n\nPlease check if KoboldAI is running at the url: "+e),selected_models=[],selected_workers=[],custom_kobold_endpoint="",render_gametext()):(uses_cors_proxy=!0,connect_custom_endpoint())}))}}else if(1==e){let e=document.getElementById("custom_oai_key").value.trim(),t=document.getElementById("custom_oai_endpoint").value.trim();""==t&&(t=document.getElementById("custom_oai_endpoint").value=default_oai_base),""!=t&&"/"==t.slice(-1)&&(t=t.slice(0,-1)),""!=t&&t.length>4&&!t.slice(-4).toLowerCase().includes("/v")&&(t+="/v1"),""!=e&&""!=t&&(hide_popups(),fetch(t+oai_models_endpoint,{method:"GET",headers:{Authorization:"Bearer "+e,"x-api-key":e},referrerPolicy:"no-referrer"}).then((e=>e.json())).then((n=>{console.log(n),!n.error&&n.data&&n.data.length>0?(custom_oai_endpoint=t,custom_oai_key=e,localsettings.saved_oai_key=custom_oai_key,localsettings.saved_oai_addr=custom_oai_endpoint,custom_oai_model=document.getElementById("custom_oai_model").value.trim(),selected_models=[{performance:100,queued:0,eta:0,name:custom_oai_model,count:1}],selected_workers=[],null==perfdata&&(perfdata={queued_requests:0,queued_tokens:0,past_minute_tokens:0,worker_count:0},document.body.classList.add("connected"),document.getElementById("connectstatus").classList.remove("color_orange"),document.getElementById("connectstatus").classList.add("color_green")),document.getElementById("connectstatus").innerHTML="Connected to OAI Endpoint",render_gametext()):(custom_oai_endpoint=default_oai_base,custom_oai_key="",msgbox(JSON.stringify(n.error.message)))})).catch((e=>{console.log("Error: "+e),custom_oai_endpoint=default_oai_base,custom_oai_key="",msgbox("Error: "+e)})))}else if(2==e){let e=document.getElementById("custom_scale_key").value.trim(),t=document.getElementById("custom_scale_ID").value.trim();t=t.split("#")[0],t=t.split("?")[0],!t.includes("dashboard.scale.com/spellbook/api/v2/deploy/")||25!=e.length||e.includes(" ")||e.includes("/")?(t="",e="",msgbox("Invalid inputs, please try again.")):t=t.split("dashboard.scale.com/spellbook/api/v2/deploy/")[1],""!=e&&""!=t&&(hide_popups(),fetch(cors_proxy+"?"+scale_submit_endpoint+t,{method:"GET",headers:{Authorization:"Bearer "+e},referrerPolicy:"no-referrer"}).then((e=>e.json())).then((n=>{console.log(n),n.message&&""!=n.message?(custom_scale_key=e,custom_scale_ID=t,selected_models=[{performance:100,queued:0,eta:0,name:"SpellbookScaleAI",count:1}],selected_workers=[],null==perfdata&&(perfdata={queued_requests:0,queued_tokens:0,past_minute_tokens:0,worker_count:0},document.body.classList.add("connected"),document.getElementById("connectstatus").classList.remove("color_orange"),document.getElementById("connectstatus").classList.add("color_green")),document.getElementById("connectstatus").innerHTML="Connected to ScaleAI Endpoint",render_gametext()):(custom_scale_key="",msgbox("Cannot connect to Spellbook by ScaleAI"))})).catch((e=>{console.log("Error: "+e),custom_scale_key="",msgbox("Error: "+e)})))}else if(3==e){let e=document.getElementById("custom_claude_key").value.trim(),t=document.getElementById("custom_claude_endpoint").value.trim();""==t&&(t=document.getElementById("custom_claude_endpoint").value=default_claude_base),""!=t&&"/"==t.slice(-1)&&(t=t.slice(0,-1)),""!=t&&t.length>4&&!t.slice(-4).toLowerCase().includes("/v")&&(t+="/v1"),""!=e&&""!=t&&(hide_popups(),custom_claude_endpoint=t,custom_claude_key=e,localsettings.saved_claude_key=custom_claude_key,localsettings.saved_claude_addr=custom_claude_endpoint,custom_claude_model=document.getElementById("custom_claude_model").value.trim(),document.getElementById("clauderenamecompat").checked&&(localsettings.instruct_starttag="Human:",localsettings.chatname="Human",localsettings.instruct_endtag="Assistant:",localsettings.chatopponent="Assistant"),selected_models=[{performance:100,queued:0,eta:0,name:custom_claude_model,count:1}],selected_workers=[],null==perfdata&&(perfdata={queued_requests:0,queued_tokens:0,past_minute_tokens:0,worker_count:0},document.body.classList.add("connected"),document.getElementById("connectstatus").classList.remove("color_orange"),document.getElementById("connectstatus").classList.add("color_green")),document.getElementById("connectstatus").innerHTML="Connected to Claude Endpoint",render_gametext())}}function display_custom_endpoint(){document.getElementById("customendpointcontainer").classList.remove("hidden")}function fetch_models(e){localmode?e(selected_models):multifetch(models_endpoints,((t,n)=>{if(t&&t.length>0){let n=[];for(let e=0;e<t.length;++e){let o=t[e].data;if(o)for(let r=0;r<o.length;++r){let s=o[r];s.cluster=t[e].cluster,n.push(s)}}e(n)}else console.log("Error: "+n),msgbox("Failed to fetch models!\nPlease check your network connection.")}))}function display_models(){document.getElementById("pickedmodel").innerHTML="",document.getElementById("loadmodelcontainer").classList.remove("hidden"),document.getElementById("apikey").value=localsettings.my_api_key;let e=!!document.getElementById("manualworker").checked,t=!1,n=!1,o=!1;function r(){if(!o)if(o=!0,e){let e="";for(let t=0;t<worker_data.length;++t){let n=worker_data[t],o=n.models&&n.models.length>0?n.models[0]:"None",r=n.name,s=find_text_horde(n.cluster),l=s&&""!=s.tag?s.tag+" ":"",a=n.trusted?'style="color:#b700ff;"':"";a=n.maintenance_mode?'style="color:#ee4444;"':a;let i=n.trusted?" 💜":"";i=n.maintenance_mode?" ⛔":i,e+="<option "+a+' value="'+t+'" '+(selected_workers.filter((e=>e.cluster==n.cluster&&e.name==n.name)).length>0?" selected":"")+">"+l+escapeHtml(r)+" ("+escapeHtml(o)+")"+i+"</option>"}document.getElementById("pickedmodel").innerHTML=e}else{let e="";for(let t=0;t<models_data.length;++t){let n=models_data[t],o=find_text_horde(n.cluster),r=o&&""!=o.tag?o.tag+" ":"",s=selected_models.filter((e=>e.cluster==n.cluster&&e.name==n.name)).length>0?" selected":"",l=parseFloat(n.performance);if(!l||isNaN(l)||l>=99999){let e=worker_data.filter((e=>e.cluster==n.cluster&&e.models.includes(n.name)));if(e.length>0){l=0;for(let t=0;t<e.length;++t){let n=e[t].performance.replace(" tokens per second","");"no requests fulfilled yet"==n.toLowerCase()&&(n=0),l+=parseFloat(n)}l/=1*e.length,l=l.toFixed(1)}}e+='<option value="'+t+'" '+s+">"+r+escapeHtml(n.name)+" (ETA: "+n.eta+"s, Queue: "+n.queued+", Speed: "+l+", Qty: "+n.count+")</option>"}e+='<option style="color:#dd7723;font-weight:bold;" value="9999">📡 [ Remote Play / Custom API Endpoint ]</option>',document.getElementById("pickedmodel").innerHTML=e}}fetch_models((e=>{models_data=e,t=!0,t&&n&&r()})),get_workers((e=>{worker_data=e,n=!0,t&&n&&r()}))}function confirm_models(){let e=Array.from(document.getElementById("pickedmodel").selectedOptions).map((({value:e})=>e));if(1==e.length&&9999==e[0])hide_popups(),display_custom_endpoint();else{custom_kobold_endpoint="",custom_oai_key="",custom_scale_key="";const o=e.indexOf("9999");if(o>-1&&e.splice(o,1),e.length>0){let o=[],r=[],s=!!document.getElementById("manualworker").checked;for(var t=0;t<e.length;++t)if(s){let s=worker_data[e[t]];r.push(s);let l=s.models;for(var n=0;n<l.length;++n){let e=models_data.find((e=>e.name==l[n]&&e.cluster==s.cluster));o.includes(e)||o.push(e)}}else{let n=models_data[e[t]];o.push(n)}o=o.filter((e=>e)),r=r.filter((e=>e));const l=o.every((e=>e.cluster===o[0].cluster)),a=r.every((e=>e.cluster===r[0].cluster));if(!l||!a)if(r.length>0){let e=get_most_common_cluster(r);r=r.filter((t=>t.cluster===e)),o=o.filter((t=>t.cluster===e))}else{let e=get_most_common_cluster(o);o=o.filter((t=>t.cluster===e))}selected_models=o,selected_workers=r,localsettings.my_api_key=document.getElementById("apikey").value,null!=localsettings.my_api_key&&""!=localsettings.my_api_key||(localsettings.my_api_key=defaultsettings.my_api_key),null!=desired_new_home_cluster&&(localsettings.home_cluster=desired_new_home_cluster,desired_new_home_cluster=null),document.getElementById("connectstatus").innerHTML="Connected to KoboldAI Horde",render_gametext(),hide_popups(),l&&a||msgbox("You've selected multiple workers from different clusters. Only one cluster will be used.","Caution")}}}function update_my_workers(){let e=document.getElementById("apikey").value,t=find_text_horde(lastValidFoundCluster);for(var n=0;n<lastValidFoundUserWorkers.length;++n){let o=document.getElementById("mwc_desc_"+n),r=document.getElementById("mwc_maint_"+n);if(o&&r&&(""!=o.value.trim()&&(null==lastValidFoundUserWorkers[n].info||lastValidFoundUserWorkers[n].info!=o.value)||""==o.value.trim()&&null!=lastValidFoundUserWorkers[n].info&&""!=lastValidFoundUserWorkers[n].info||r.checked!=lastValidFoundUserWorkers[n].maintenance_mode)){console.log("updating worker "+lastValidFoundUserWorkers[n].id);let s={maintenance:r.checked};(""!=o.value.trim()||""==o.value.trim()&&null!=lastValidFoundUserWorkers[n].info&&""!=lastValidFoundUserWorkers[n].info)&&(s.info=o.value.trim()),fetch(t.maintenance_endpoint+"/"+lastValidFoundUserWorkers[n].id,{method:"PUT",headers:{"Content-Type":"application/json",apikey:e},body:JSON.stringify(s)}).then((e=>e.json())).then((e=>{msgbox(JSON.stringify(e),"Update My Worker")})).catch((e=>{console.error("Error:",e)}))}}}let desired_new_home_cluster=null,lastValidFoundUserData=null,lastValidFoundCluster=null,lastValidFoundUserWorkers=[];function fetch_kudo_balance(){if(localmode)return;desired_new_home_cluster=null;let e=document.getElementById("apikey").value;if(null!=e&&""!=e.trim()){document.getElementById("kudos_bal").innerHTML="Checking...<br>&nbsp;";let t={method:"GET",headers:{apikey:e}};multifetch(finduser_endpoints.map((e=>[e,t])),((e,t)=>{if(e&&e.length>0){lastValidFoundUserData=null,lastValidFoundCluster="";for(let t=0;t<e.length;++t){let n=e[t].data,o=e[t].cluster;if(n){let e=n.username;if(console.log(n),null!=e&&""!=e){lastValidFoundUserData=n,lastValidFoundCluster=o;break}}}if(lastValidFoundUserData){desired_new_home_cluster=lastValidFoundCluster;let e=lastValidFoundUserData.kudos,t=lastValidFoundUserData.username,n=find_text_horde(desired_new_home_cluster),o=n&&""!=n.tag?n.tag+" ":"",r="<a class='color_blueurl' href='#' onclick='show_my_own_workers()'>"+t+"</a>";e<0?(document.getElementById("kudos_bal").innerHTML=o+r+"<br>Kudos Balance: 0","anonymous#0"==t.toLowerCase()&&(document.getElementById("kudos_bal").innerHTML=o+t+"<br><a class='color_blueurl' href='https://horde.koboldai.net/register'>(Register New User)</a>")):document.getElementById("kudos_bal").innerHTML=o+r+"<br>Kudos Balance: "+e}else document.getElementById("kudos_bal").innerHTML="API Key Error<br><a class='color_blueurl' href='https://horde.koboldai.net/register'>(Register New User)</a>"}else console.log("Error: "+t),document.getElementById("kudos_bal").innerHTML="API Key Error<br><a class='color_blueurl' href='https://horde.koboldai.net/register'>(Register New User)</a>"}))}}function focus_api_keys(){var e=document.getElementById("apikey");e&&"password"===e.type&&(e.type="text"),(e=document.getElementById("custom_oai_key"))&&"password"===e.type&&(e.type="text"),(e=document.getElementById("custom_claude_key"))&&"password"===e.type&&(e.type="text")}function blur_api_keys(){var e=document.getElementById("apikey");e&&"text"===e.type&&(e.type="password"),(e=document.getElementById("custom_oai_key"))&&"text"===e.type&&(e.type="password"),(e=document.getElementById("custom_claude_key"))&&"text"===e.type&&(e.type="password")}function display_settings(){document.getElementById("settingscontainer").classList.remove("hidden"),document.getElementById("max_context_length").value=document.getElementById("max_context_length_slide").value=localsettings.max_context_length,document.getElementById("max_length").value=document.getElementById("max_length_slide").value=localsettings.max_length,document.getElementById("temperature").value=document.getElementById("temperature_slide").value=localsettings.temperature,document.getElementById("rep_pen").value=document.getElementById("rep_pen_slide").value=localsettings.rep_pen,document.getElementById("rep_pen_slope").value=localsettings.rep_pen_slope,document.getElementById("rep_pen_range").value=localsettings.rep_pen_range,document.getElementById("top_p").value=document.getElementById("top_p_slide").value=localsettings.top_p,document.getElementById("autoscroll").checked=localsettings.autoscroll,document.getElementById("export_settings").checked=localsettings.export_settings,document.getElementById("invert_colors").checked=localsettings.invert_colors,document.getElementById("trimsentences").checked=localsettings.trimsentences,document.getElementById("trimwhitespace").checked=localsettings.trimwhitespace,document.getElementById("persist_session").checked=localsettings.persist_session,document.getElementById("opmode").value=localsettings.opmode,document.getElementById("chatname").value=localsettings.chatname,document.getElementById("chatopponent").value=localsettings.chatopponent,document.getElementById("instruct_starttag").value=localsettings.instruct_starttag,document.getElementById("instruct_endtag").value=localsettings.instruct_endtag,document.getElementById("top_k").value=localsettings.top_k,document.getElementById("top_a").value=localsettings.top_a,document.getElementById("typ_s").value=localsettings.typ_s,document.getElementById("tfs_s").value=localsettings.tfs_s,document.getElementById("generate_images").value=localsettings.generate_images,document.getElementById("enhanced_chat_ui").checked=localsettings.enhanced_chat_ui,document.getElementById("multiline_replies").checked=localsettings.multiline_replies,document.getElementById("allow_continue_chat").checked=localsettings.allow_continue_chat,document.getElementById("idle_responses").value=localsettings.idle_responses,document.getElementById("idle_duration").value=localsettings.idle_duration,document.getElementById("adventure_context_mod").checked=localsettings.adventure_context_mod,document.getElementById("instruct_has_markdown").checked=localsettings.instruct_has_markdown,document.getElementById("auto_ctxlen").checked=localsettings.auto_ctxlen,document.getElementById("auto_genamt").checked=localsettings.auto_genamt,pendingstyle=localsettings.image_styles;let e=localsettings.sampler_order.toString();document.getElementById("sampler_order").value=e;let t="";for(var n=0;n<presets.length;++n)t+='<option value="'+n+'" title="'+presets[n].description+'">'+presets[n].preset+"</option>";t+='<option value="9999" title="User Defined Settings">[Custom]</option>',document.getElementById("presets").innerHTML=t,document.getElementById("presets").value=localsettings.last_selected_preset;var o='<option value="0">Disabled</option>';if("speechSynthesis"in window){let e=window.speechSynthesis.getVoices();console.log("speech synth available: "+e.length);for(n=0;n<e.length;++n)o+='<option value="'+(n+1)+'">'+e[n].name+"</option>"}else console.log("No speech synth available");document.getElementById("ttsselect").innerHTML=o,document.getElementById("ttsselect").value=localsettings.speech_synth,document.getElementById("beep_on").checked=localsettings.beep_on,toggle_opmode();let r="";for(n=0;n<stablemodels.length;++n)r+='<option value="'+stablemodels[n].name+" ("+stablemodels[n].count+')">';document.getElementById("sdmodels").innerHTML=r,document.getElementById("img_autogen").checked=localsettings.img_autogen,document.getElementById("save_images").checked=localsettings.save_images,document.getElementById("img_allownsfw").checked=localsettings.img_allownsfw}function toggle_preset(){let e=document.getElementById("presets").value,t=presets[e];t&&(temp_changingpreset=!0,document.getElementById("temperature").value=document.getElementById("temperature_slide").value=t.temp,document.getElementById("max_length").value=document.getElementById("max_length_slide").value=t.genamt,document.getElementById("top_k").value=t.top_k,document.getElementById("top_p").value=document.getElementById("top_p_slide").value=t.top_p,document.getElementById("top_a").value=t.top_a,document.getElementById("typ_s").value=t.typical,document.getElementById("tfs_s").value=t.tfs,document.getElementById("rep_pen").value=document.getElementById("rep_pen_slide").value=t.rep_pen,document.getElementById("rep_pen_range").value=t.rep_pen_range,document.getElementById("rep_pen_slope").value=t.rep_pen_slope,document.getElementById("sampler_order").value=t.sampler_order.toString())}function validate_sd_model(){var e=document.getElementById("generate_images").value;let t=!1;for(var n=0;n<stablemodels.length;++n){if(e==stablemodels[n].name+" ("+stablemodels[n].count+")"||e==stablemodels[n].name){document.getElementById("generate_images").value=stablemodels[n].name,t=!0;break}}t||"*"==e||(document.getElementById("generate_images").value="")}function clear_sd_model(){document.getElementById("generate_images").value="",image_models_fetched||fetch_image_models(display_settings)}function validate_samplers(e=!1){let t=document.getElementById("sampler_order").value.split(","),n=[0,1,2,3,4,5,6],o=!0;for(a in t){let e=parseInt(t[a],10);!isNaN(e)&&n.includes(e)?(t[a]=e,n[e]=void 0):o=!1}7==t.length&&o?(e&&(localsettings.sampler_order=t),document.getElementById("sampler_order").value=t.toString()):(e&&(localsettings.sampler_order=defaultsettings.sampler_order),document.getElementById("sampler_order").value=defaultsettings.sampler_order.toString())}var temp_changingpreset=!1;function setting_tweaked(){temp_changingpreset?temp_changingpreset=!1:document.getElementById("presets").value=9999}function toggle_invert_colors(){localsettings.invert_colors?document.body.classList.add("invert_colors"):document.body.classList.remove("invert_colors")}function confirm_settings(){localsettings.max_context_length=document.getElementById("max_context_length").value,localsettings.max_length=document.getElementById("max_length").value,localsettings.temperature=document.getElementById("temperature").value,localsettings.rep_pen=document.getElementById("rep_pen").value,localsettings.rep_pen_slope=document.getElementById("rep_pen_slope").value,localsettings.rep_pen_range=document.getElementById("rep_pen_range").value,localsettings.top_p=document.getElementById("top_p").value,localsettings.autoscroll=!!document.getElementById("autoscroll").checked,localsettings.export_settings=!!document.getElementById("export_settings").checked,localsettings.invert_colors=!!document.getElementById("invert_colors").checked,localsettings.trimsentences=!!document.getElementById("trimsentences").checked,localsettings.trimwhitespace=!!document.getElementById("trimwhitespace").checked,localsettings.persist_session=!!document.getElementById("persist_session").checked,localsettings.enhanced_chat_ui=!!document.getElementById("enhanced_chat_ui").checked,localsettings.multiline_replies=!!document.getElementById("multiline_replies").checked,localsettings.allow_continue_chat=!!document.getElementById("allow_continue_chat").checked,localsettings.idle_responses=document.getElementById("idle_responses").value,localsettings.idle_duration=document.getElementById("idle_duration").value,localsettings.adventure_context_mod=!!document.getElementById("adventure_context_mod").checked,localsettings.instruct_has_markdown=!!document.getElementById("instruct_has_markdown").checked,localsettings.generate_images=document.getElementById("generate_images").value,localsettings.opmode=document.getElementById("opmode").value,localsettings.chatname=document.getElementById("chatname").value,null!=localsettings.chatname&&""!=localsettings.chatname||(localsettings.chatname="You"),localsettings.chatopponent=document.getElementById("chatopponent").value.trim(),localsettings.instruct_starttag=document.getElementById("instruct_starttag").value,null!=localsettings.instruct_starttag&&""!=localsettings.instruct_starttag||(localsettings.instruct_starttag="\\n### Instruction:\\n"),localsettings.instruct_endtag=document.getElementById("instruct_endtag").value,null!=localsettings.instruct_endtag&&""!=localsettings.instruct_endtag||(localsettings.instruct_endtag="\\n### Response:\\n"),localsettings.top_k=document.getElementById("top_k").value,localsettings.top_a=document.getElementById("top_a").value,localsettings.typ_s=document.getElementById("typ_s").value,localsettings.tfs_s=document.getElementById("tfs_s").value,localsettings.speech_synth=document.getElementById("ttsselect").value,localsettings.beep_on=!!document.getElementById("beep_on").checked,localsettings.auto_ctxlen=!!document.getElementById("auto_ctxlen").checked,localsettings.auto_genamt=!!document.getElementById("auto_genamt").checked,localsettings.image_styles=pendingstyle,localsettings.img_autogen=!!document.getElementById("img_autogen").checked,localsettings.save_images=!!document.getElementById("save_images").checked,localsettings.img_allownsfw=!!document.getElementById("img_allownsfw").checked,localsettings.generate_images?(document.getElementById("btn_genimg").classList.remove("hidden"),document.getElementById("btn_genimg2").classList.remove("hidden")):(document.getElementById("btn_genimg").classList.add("hidden"),document.getElementById("btn_genimg2").classList.add("hidden")),localsettings.enhanced_chat_ui&&3==localsettings.opmode&&document.getElementById("allowediting")&&(document.getElementById("allowediting").checked=!1,toggle_editable()),validate_samplers(!0),localsettings.last_selected_preset=document.getElementById("presets").value,localsettings.max_context_length=cleannum(localsettings.max_context_length,8,99999),localsettings.max_length=cleannum(localsettings.max_length,1,localsettings.max_context_length-1),localsettings.temperature=cleannum(localsettings.temperature,.01,5),localsettings.rep_pen=cleannum(localsettings.rep_pen,.1,5),localsettings.rep_pen_range=cleannum(localsettings.rep_pen_range,0,8192),localsettings.rep_pen_slope=cleannum(localsettings.rep_pen_slope,0,20),localsettings.top_p=cleannum(localsettings.top_p,.002,1),localsettings.top_k=cleannum(Math.floor(localsettings.top_k),0,300),localsettings.top_a=cleannum(localsettings.top_a,0,1),localsettings.typ_s=cleannum(localsettings.typ_s,0,1),localsettings.tfs_s=cleannum(localsettings.tfs_s,0,1),toggle_invert_colors(),autosave(),hide_popups(),render_gametext()}function toggle_opmode(){document.getElementById("chatnamesection").classList.add("hidden"),document.getElementById("adventuresection").classList.add("hidden"),document.getElementById("instructsection").classList.add("hidden"),document.getElementById("idlesection").classList.add("hidden"),1==document.getElementById("opmode").value&&document.getElementById("idlesection").classList.remove("hidden"),3==document.getElementById("opmode").value&&(document.getElementById("chatnamesection").classList.remove("hidden"),document.getElementById("idlesection").classList.remove("hidden")),2==document.getElementById("opmode").value&&document.getElementById("adventuresection").classList.remove("hidden"),4==document.getElementById("opmode").value&&document.getElementById("instructsection").classList.remove("hidden")}function prompt_overwrite(){msgboxYesNo("You already have an existing persistent story. Do you want to overwrite it?","Overwrite Warning",confirm_overwrite,hide_popups)}function confirm_overwrite(){pending_found_story&&""!=pending_found_story&&(import_share_story(pending_found_story),pending_found_story=null),hide_popups()}function display_newgame(){document.getElementById("newgamecontainer").classList.remove("hidden")}function confirm_newgame(){localmode||document.getElementById("keep_ai_selected").checked||(selected_models=[],selected_workers=[],localsettings.opmode=1),restart_new_game(),hide_popups()}function confirm_memory(){current_memory=document.getElementById("memorytext").value,current_anote=document.getElementById("anotetext").value,current_anotetemplate=document.getElementById("anotetemplate").value,anote_strength=document.getElementById("anote_strength").value,extrastopseq=document.getElementById("extrastopseq").value,hide_popups(),render_gametext()}let temp_automem_store="";function autogenerate_summary_memory(){temp_automem_store=document.getElementById("memorytext").value;let e=()=>{pending_response_id="-1",waiting_for_autosummary=!0;let e=Math.floor(3.35*localsettings.max_context_length)-100,t=concat_gametext(!0,"");t=end_trim_to_sentence(t,!0),t.substring(t.length-e);let n=t.length>1800;t+="\n### Instruction:Summarize the above text in a single paragraph of up to "+(n?"ten":"five")+" detailed sentences.\n### Response:";let o={prompt:t,params:{n:1,max_context_length:localsettings.max_context_length,max_length:n?200:150,rep_pen:localsettings.rep_pen,temperature:localsettings.temperature,top_p:localsettings.top_p,top_k:localsettings.top_k,top_a:localsettings.top_a,typical:localsettings.typ_s,tfs:localsettings.tfs_s,rep_pen_range:localsettings.rep_pen_range,rep_pen_slope:localsettings.rep_pen_slope,sampler_order:localsettings.sampler_order},models:selected_models.map((e=>e.name))};o.workers=selected_workers.map((e=>e.id)),dispatch_submit_generation(o),render_gametext(),document.getElementById("memorytext").value="[<|Generating summary, do not close window...|>]"};0==gametext_arr.length||1==gametext_arr.length&&""==gametext_arr[0].trim()?console.log("Cannot summarize nothing."):""!=temp_automem_store.trim()?msgboxYesNo("This will modify existing memory. Proceed?","Confirm Modify",(()=>{document.getElementById("yesnocontainer").classList.add("hidden"),e()}),(()=>{document.getElementById("yesnocontainer").classList.add("hidden")})):e()}function handle_incoming_autosummary(e){waiting_for_autosummary=!1;let t=(e=replaceAll(e=(e=e.trim()).split("###")[0],"\n\n","\n")).split("\n"),n=200;if((e=t[0]).length<100&&t.length>1)for(var o=1;o<t.length&&(n-=t[o].length,t[o].trim().length>5&&(e+="\n"+t[o]),!(n<=0));++o);e=end_trim_to_sentence(e,!0),""==temp_automem_store.trim()?document.getElementById("memorytext").value="[Summary: "+e+"]":document.getElementById("memorytext").value=temp_automem_store+"\n\n[Summary Continued: "+e+"]"}function clear_poll_flags(){pending_response_id="",poll_in_progress=!1,synchro_polled_response=null,synchro_pending_stream="",waiting_for_autosummary=!1}function restart_new_game(){idle_timer=0,gametext_arr=[],redo_arr=[],last_request_str="No Requests Available",retry_prev_text="",redo_prev_text="",nextgeneratedimagemilestone=generateimagesinterval,pending_response_id="",synchro_polled_response=null,synchro_pending_stream="",waiting_for_autosummary=!1,current_memory="",current_anote="",current_wi=[],pending_context_preinjection="",current_anotetemplate="[Author's note: <|>]",loaded_storyobj=generate_base_storyobj(),document.getElementById("input_text").value="",document.getElementById("cht_inp").value="",image_db={},completed_imgs_meta={},localsettings.adventure_is_action=!1,prev_hl_chunk=null,last_token_budget="",last_known_filename="",render_gametext()}function btn_editmode(){gametext_arr.length>0&&(document.getElementById("allowediting").checked=!0,toggle_editable())}function toggle_editable(){0==gametext_arr.length?selected_models.length>0||selected_workers.length>0?document.getElementById("allowediting").checked&&gametext_arr.push(""):document.getElementById("allowediting").checked=!1:1==gametext_arr.length&&""==gametext_arr[0]&&gametext_arr.pop(),render_gametext()}function end_trim_to_sentence(e,t=!1){let n=-1,o=[".","!","?","`","*",'"',")","}","`","]",";"];for(let t=0;t<o.length;++t)n=Math.max(n,e.lastIndexOf(o[t]));if(t){let t=e.lastIndexOf("\n");n=Math.max(n,t)}return n>0?e.substring(0,n+1).trimEnd():e.trimEnd()}function start_trim_to_sentence(e){let t=e.indexOf("."),n=e.indexOf("!"),o=e.indexOf("?"),r=e.indexOf("\n"),s=t,l=!1;return n>0&&n<s&&(s=n),o>0&&o<s&&(s=o),r>0&&r<s&&(s=r,l=!0),s>0?l?e.substring(s+1):e.substring(s+2):e}function handle_typing(e){var t=(e=e||window.event).keyCode||e.which;if(!e.shiftKey&&13==t){let t=!!document.getElementById("entersubmit").checked,n=""!=document.getElementById("input_text").value;t&&(e.preventDefault(),n&&!document.getElementById("btnsend").disabled&&submit_generation())}}function show_abort_button(e){e?(document.getElementById("abortgen").classList.remove("hidden"),document.getElementById("chat_msg_send_btn_abort").classList.remove("hidden")):(document.getElementById("abortgen").classList.add("hidden"),document.getElementById("chat_msg_send_btn_abort").classList.add("hidden"))}function abort_generation(){let e=pending_response_id;if(is_using_custom_ep()&&""!=pending_response_id&&""!=synchro_pending_stream&&(synchro_polled_response=synchro_pending_stream,poll_in_progress=!1,poll_pending_response()),console.log("Generation "+pending_response_id+" aborted"),clear_poll_flags(),render_gametext(),pending_response_horde&&e&&""!=e&&!is_using_custom_ep()){let t=pending_response_horde.output_endpoint+"/"+e;fetch(t,{method:"DELETE"}).then((e=>e.json())).then((e=>{console.log(e)})).catch((e=>{console.error("Error:",e)}))}else is_using_newer_kcpp()&&fetch(custom_kobold_endpoint+koboldcpp_abort_endpoint,{method:"POST",headers:{"Content-Type":"application/json"}}).then((e=>e.json())).then((e=>{})).catch((e=>{console.error("Error:",e)}));show_abort_button(!1)}function manual_gen_image(){let e=concat_gametext(!0,"");var t=e.length;if(t>0){var n=e.substring(t-300,t);(n=end_trim_to_sentence(n=start_trim_to_sentence(n),!0)).length>0&&(generate_new_image(n),nextgeneratedimagemilestone=t+generateimagesinterval,document.getElementById("btn_genimg").disabled=!0,document.getElementById("btn_genimg2").disabled=!0,setTimeout((()=>{document.getElementById("btn_genimg").disabled=!1,document.getElementById("btn_genimg2").disabled=!1}),1e4))}}function submit_generation(){let e=document.getElementById("input_text").value,t=!1;if(""!=e.trim()||gametext_arr.length>0||""!=current_memory||""!=current_anote){if(waiting_for_autosummary=!1,idle_timer=0,idle_triggered_counter=0,localsettings.speech_synth>0&&"speechSynthesis"in window){let t=new window.SpeechSynthesisUtterance(e);t.voice=window.speechSynthesis.getVoices()[localsettings.speech_synth-1],window.speechSynthesis.speak(t)}4==localsettings.opmode&&""!=e&&(e=get_instruct_starttag(!1)+e+get_instruct_endtag(!1)),3==localsettings.opmode&&""!=e?e="\n"+localsettings.chatname+": "+e:3==localsettings.opmode&&""==e.trim()&&(e=""),2==localsettings.opmode&&""!=e&&localsettings.adventure_is_action&&(e="\n\n> "+e+"\n\n"),2==localsettings.opmode&&""!=e&&0==gametext_arr.length&&(localsettings.adventure_is_action||(localsettings.adventure_is_action=!0,""==current_memory.trim()&&(t=!0))),""!=e&&gametext_arr.push(e),redo_arr=[],retry_prev_text="",redo_prev_text="",document.getElementById("input_text").value="",pending_response_id="-1";let l=document.getElementById("maintxtloader");if(l){l.classList.remove("greenloader"),l.classList.remove("redloader");let e=document.getElementById("outerloadernum");e&&(e.innerText="")}let a=localsettings.max_context_length,i=localsettings.max_length;if(!is_using_custom_ep()&&(localsettings.auto_genamt||localsettings.auto_ctxlen)){let e=selected_workers;if((null==e||0==e.length)&&selected_models&&selected_models.length>0){e=[];for(let t=0;t<worker_data.length;++t){let n=worker_data[t];for(let t=0;t<selected_models.length;++t){let o=selected_models[t];if(o.cluster==n.cluster&&n.models.includes(o.name)){e.push(n);break}}}}for(let t=0;t<e.length;++t){let n=e[t];localsettings.auto_ctxlen&&(a=Math.min(n.max_context_length,a)),localsettings.auto_genamt&&(i=Math.min(n.max_length,i))}}let c=Math.floor(3.35*a);null!=current_memory&&""!=current_memory.trim()||(c=Math.floor(6*a));let d=concat_gametext(!0,"");if(d=d.replace(/\xA0/g," "),localsettings.trimwhitespace&&(d=d.replace(/[\t ]+$/,""),d=d.replace(/[\r\n]+/g,"\n")),2==localsettings.opmode&&localsettings.adventure_context_mod){let e="[Interactive Fiction: Game Mode Enabled]\n[You are playing a choose-your-own-adventure game. Please input action.]\n";e+="\n\n> Look\n\nYou look around, observing yourself and your surroundings.\n\n",d=e+d}if(3==localsettings.opmode){let t=localsettings.chatopponent,r=!1;if(t.includes("||$||")){let e=t.split("||$||");e=e.filter((e=>e&&""!=e)),e=e.map((e=>e.trim())),t=e[Math.floor(Math.random()*e.length)],r=e.length>1}let s=localsettings.chatname;null!=t&&""!=t||(t="");var n=new RegExp("\n(?!"+localsettings.chatname+").+?: ","gi"),o=(current_memory+current_anote+d).match(n);if(""==t&&null!=o&&o.length>0){let e=o[0].replace(": ","");e=e.trim(),""!=e&&(t=e)}if(0==current_anote.length&&0==current_memory.length&&gametext_arr.length>0&&gametext_arr[0].startsWith("\n"+s+": ")){let e="[The following is an interesting chat message log between "+s+" and "+t+".]\n\n"+localsettings.chatname+": Hi.\n"+t+": Hello.";""==t&&(e="[The following is an interesting chat message log between "+s+" and someone else.]\n\n"+localsettings.chatname+": Hi."),r&&(e="[The following is an interesting chat message log between "+s+" and multiple others.]\n\n"+localsettings.chatname+": Hi."),d=e+d}""!=t?(t=replaceAll(t,"\n",""),pending_context_preinjection="\n"+t+":"):pending_context_preinjection="\n",localsettings.allow_continue_chat&&""==e.trim()?pending_context_preinjection="":d+=pending_context_preinjection}if(""!=localsettings.generate_images&&3!=localsettings.opmode&&4!=localsettings.opmode&&localsettings.img_autogen)if(2==localsettings.opmode){if(e.startsWith("\n\n> "))generate_new_image(start_trim_to_sentence(d.substring(r-200,r)))}else{var r=d.length;if(r>nextgeneratedimagemilestone)nextgeneratedimagemilestone=r+generateimagesinterval,generate_new_image(end_trim_to_sentence(start_trim_to_sentence(d.substring(r-300,r)),!0))}let m=Math.floor(.9*c),u=current_memory.substring(current_memory.length-m);null!=u&&""!=u&&(u+="\n");let _=d;if(localsettings.case_sensitive_wi||(_=_.toLowerCase()),current_wi.length>0)for(var s=0;s<current_wi.length;++s){let e=current_wi[s];if(null==e.key||""==e.key)continue;let t=e.selective&&(""==e.keysecondary||null==e.keysecondary),n=e.key.split(","),o=!1;if(e.constant)o=!0;else if(!e.selective||t)o=localsettings.case_sensitive_wi?n.some((e=>_.includes(e.trim()))):n.some((e=>_.includes(e.trim().toLowerCase())));else{let t=e.keysecondary.split(",");if(localsettings.case_sensitive_wi){let e=n.some((e=>_.includes(e.trim()))),r=t.some((e=>_.includes(e.trim())));o=e&&r}else{let e=n.some((e=>_.includes(e.trim().toLowerCase()))),r=t.some((e=>_.includes(e.trim().toLowerCase())));o=e&&r}}o&&(u+=e.content+"\n")}let g=current_anotetemplate.replace("<|>",current_anote);if(g=g.substring(g.length-m),0==current_anote.length&&(g=""),u.length>0||current_anote.length>0){d=d.substring(d.length-c);let e=u.length+d.length+g.length-c;d=d.substring(e);let t=anote_strength,n=d.length-t;for(let e=0;e<10&&(n>=0&&n<d.length&&" "!=d[n]&&"."!=d[n]&&"!"!=d[n]&&"?"!=d[n]&&"\n"!=d[n]);++e)++n;n=clamp(n,0,d.length),d=d.slice(0,n)+g+d.slice(n),d=u+d}last_token_budget=d.length+"/"+c;let p={prompt:d,params:{n:1,max_context_length:a,max_length:i,rep_pen:localsettings.rep_pen,temperature:localsettings.temperature,top_p:localsettings.top_p,top_k:localsettings.top_k,top_a:localsettings.top_a,typical:localsettings.typ_s,tfs:localsettings.tfs_s,rep_pen_range:localsettings.rep_pen_range,rep_pen_slope:localsettings.rep_pen_slope,sampler_order:localsettings.sampler_order},models:selected_models.map((e=>e.name))};p.workers=selected_workers.map((e=>e.id)),t?pending_response_id="":dispatch_submit_generation(p),render_gametext()}}function dispatch_submit_generation(e){if(console.log(e),last_request_str=JSON.stringify(e),startTimeTaken(),is_using_custom_ep())if(console.log("submit custom api"),pending_response_id="submit-v1-dummy-id",poll_ticks_passed=0,poll_in_progress=!1,synchro_polled_response=null,synchro_pending_stream="",""!=custom_kobold_endpoint){let t=e.prompt;(e=e.params).prompt=t;let n=!!document.getElementById("remoteconsolelog").checked;if(e.quiet=!n,kobold_endpoint_version&&""!=kobold_endpoint_version&&compare_version_str(kobold_endpoint_version,"1.2.1")>0){if(2==localsettings.opmode&&(e.stop_sequence=["\n> "]),3==localsettings.opmode&&(e.stop_sequence=[localsettings.chatname+":","\n"+localsettings.chatname+" "],localsettings.chatopponent.includes("||$||"))){let t=localsettings.chatopponent.split("||$||");t=t.filter((e=>e&&""!=e)),t=t.map((e=>e.trim()));for(let n=0;n<t.length;++n)e.stop_sequence.push(t[n]+":")}if(4==localsettings.opmode){let t=get_instruct_starttag(!0),n=get_instruct_endtag(!0);e.stop_sequence=[t,n]}if(""!=extrastopseq){let t=replaceAll(extrastopseq,"\\n","\n").split("||$||");t.length>0&&!e.stop_sequence&&(e.stop_sequence=[]);for(let n=0;n<t.length;++n)t[n]&&""!=t[n]&&e.stop_sequence.push(t[n])}}let o=should_use_pseudostreaming(),r=urlParams.get("streamamount"),s=8;null!=r&&r>0&&(s=r),kobold_api_stream(apply_proxy_url(custom_kobold_endpoint+kobold_custom_gen_endpoint),e,e.max_length,"",o?s:4096)}else if(""!=custom_oai_key){let t=custom_oai_endpoint+oai_submit_endpoint,n=e.params.rep_pen-1,o={max_tokens:e.params.max_length,model:custom_oai_model,presence_penalty:n,temperature:e.params.temperature,top_p:e.params.top_p,logit_bias:{50256:-100}};"gpt-3.5-turbo"==custom_oai_model||"gpt-3.5-turbo-16k"==custom_oai_model||"gpt-4"==custom_oai_model||"gpt-4-32k"==custom_oai_model?(t=custom_oai_endpoint+oai_submit_endpoint_turbo,document.getElementById("jailbreakprompt")&&document.getElementById("jailbreakprompt").checked&&""!=document.getElementById("jailbreakprompttext").value?o.messages=[{role:"system",content:document.getElementById("jailbreakprompttext").value},{role:"user",content:e.prompt}]:o.messages=[{role:"user",content:e.prompt}]):o.prompt=e.prompt,fetch(t,{method:"POST",headers:{"Content-Type":"application/json","x-api-key":custom_oai_key,Authorization:"Bearer "+custom_oai_key},body:JSON.stringify(o),referrerPolicy:"no-referrer"}).then((e=>e.json())).then((e=>{if(console.log("sync finished response: "+JSON.stringify(e)),""!=custom_oai_key&&null!=e.choices&&e.choices.length>0){let t=e.choices[0];t.text?synchro_polled_response=t.text:t.message?synchro_polled_response=t.message.content:(console.error("Error, unknown OAI response"),clear_poll_flags(),render_gametext(),msgbox("Error, unknown OAI response"))}else console.error("error occurred in OAI generation"),clear_poll_flags(),render_gametext(),msgbox("Error occurred during text generation: "+formatError(e))})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),msgbox("Error while submitting prompt: "+e)}))}else if(""!=custom_scale_key){let t=cors_proxy+"?"+scale_submit_endpoint+custom_scale_ID,n={input:{input:e.prompt}};fetch(t,{method:"POST",headers:{"Content-Type":"application/json",Authorization:"Basic "+custom_scale_key},body:JSON.stringify(n),referrerPolicy:"no-referrer"}).then((e=>e.json())).then((e=>{console.log("sync finished response: "+JSON.stringify(e)),""!=custom_scale_key&&null!=e.output&&""!=e.output?synchro_polled_response=e.output:(console.error("error occurred in Scale generation"),clear_poll_flags(),render_gametext(),msgbox("Error occurred during text generation: "+formatError(e)))})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),msgbox("Error while submitting prompt: "+e)}))}else if(""!=custom_claude_key){let t=cors_proxy+"?"+(custom_claude_endpoint+claude_submit_endpoint),n={prompt:e.prompt,max_tokens_to_sample:e.params.max_length,model:custom_claude_model,top_k:e.params.top_k<=0?-1:e.params.top_k,temperature:e.params.temperature,top_p:e.params.top_p};fetch(t,{method:"POST",headers:{"Content-Type":"application/json","x-api-key":custom_claude_key,Authorization:"Bearer "+custom_claude_key},body:JSON.stringify(n),referrerPolicy:"no-referrer"}).then((e=>e.json())).then((e=>{console.log("sync finished response: "+JSON.stringify(e)),""!=custom_claude_key&&null!=e.completion&&""!=e.completion?synchro_polled_response=e.completion:(console.error("error occurred in Claude generation"),clear_poll_flags(),render_gametext(),msgbox("Error occurred during text generation: "+formatError(e)))})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),msgbox("Error while submitting prompt: "+e)}))}else console.log("Unknown sync endpoint!");else{console.log("submit v2 api");let t=find_text_horde(localsettings.home_cluster);if(selected_workers.length>0){const e=selected_workers.filter((e=>e.cluster==localsettings.home_cluster)),n=selected_workers.filter((e=>e.cluster!=localsettings.home_cluster));if(0==e.length&&n.length>0){let e=find_text_horde(n[0].cluster);e&&(t=e)}}else if(selected_models.length>0){const e=selected_models.filter((e=>e.cluster==localsettings.home_cluster)),n=selected_models.filter((e=>e.cluster!=localsettings.home_cluster));if(0==e.length&&n.length>0){let e=find_text_horde(n[0].cluster);e&&(t=e)}}let n=t.baseurl==localsettings.home_cluster?localsettings.my_api_key:defaultsettings.my_api_key,o=t.client_agent,r={"Content-Type":"application/json",apikey:n};null!=o&&(r["Client-Agent"]=o),fetch(t.submit_endpoint,{method:"POST",headers:r,body:JSON.stringify(e)}).then((e=>e.json())).then((e=>{console.log("Success:",e),e.id&&""!=e.id?(pending_response_id=e.id,pending_response_horde=t,poll_ticks_passed=0,console.log("awaiting response for "+pending_response_id)):(clear_poll_flags(),render_gametext(),""!=e.message?msgbox(e.message):msgbox("Unspecified error while submitting prompt"))})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),msgbox("Error while submitting prompt: "+e)}))}}function sanitize_horde_image_prompt(e){if(null==e||""==e)return"";return(e=(e=(e=(e=(e=e.replace(/\b(girl)\b/gim,"woman")).replace(/\b(boy)\b/gim,"man")).replace(/\b(girls)\b/gim,"women")).replace(/\b(boys)\b/gim,"men")).replace(/\b(under.age|under.aged|underage|underaged|loli|pedo|pedophile|(\w+).year.old|(\w+).years.old|minor|prepubescent|minors|shota)\b/gim,"")).match(/\b(cock|ahegao|hentai|uncensored|lewd|cocks|deepthroat|deepthroating|dick|dicks|cumshot|lesbian|fuck|fucked|fucking|sperm|naked|nipples|tits|boobs|breasts|boob|breast|topless|ass|butt|fingering|masturbate|masturbating|bitch|blowjob|pussy|piss|asshole|dildo|dildos|vibrator|erection|foreskin|handjob|nude|penis|porn|vibrator|virgin|vagina|vulva|threesome|orgy|bdsm|hickey|condom|testicles|anal|bareback|bukkake|creampie|stripper|strap-on|missionary|clitoris|clit|clitty|cowgirl|fleshlight|sex|buttplug|milf|oral|sucking|bondage|orgasm|scissoring|railed|slut|sluts|slutty|cumming|cunt|faggot|sissy|anal|anus|cum|semen|scat|nsfw|xxx|explicit|erotic|horny|aroused|jizz|moan|rape|raped|raping|throbbing|humping)\b/gim)&&(e=(e=e.replace(/\b(youngster|infant|baby|toddler|child|teen|kid|kiddie|kiddo|teenager|student|preteen|pre.teen)\b/gim,"person")).replace(/\b(young|younger|youthful|youth|small|smaller|smallest|girly|boyish|lil|tiny|teenaged|lit[tl]le|school.aged|school|highschool|kindergarten|teens|children|kids)\b/gim,"")),e}function generate_new_image(e){localsettings.image_styles&&""!=localsettings.image_styles&&(e=localsettings.image_styles+" "+e),filter_enabled&&(e=sanitize_horde_image_prompt(e)),console.log("Generating image for: "+e);let t=[];t="*"==localsettings.generate_images?[]:[localsettings.generate_images];let n={prompt:e+" ### disfigured, ugly, deformed, poorly, censor, censored, blurry, lowres, fused, malformed, watermark, misshapen, duplicated, grainy, distorted, signature",params:{cfg_scale:7,sampler_name:"k_euler_a",height:512,width:512,steps:20,karras:!1,n:1,seed:"",post_processing:[]},models:t,nsfw:!!localsettings.img_allownsfw,censor_nsfw:!localsettings.img_allownsfw,trusted_workers:!1,replacement_filter:!0,r2:!1};fetch(stablehorde_submit_endpoint,{method:"POST",headers:{"Content-Type":"application/json","Client-Agent":default_client_agent,apikey:localsettings.my_api_key},body:JSON.stringify(n)}).then((e=>e.json())).then((t=>{if(console.log("genimg result:",t),t.id&&""!=t.id){let n="[<|p|"+t.id+"|p|>]";gametext_arr.push(n),image_db[t.id]={done:!1,queue:"Starting",result:"",alt:e},console.log("New image queued "+n)}else msgbox("Image generation failed: "+t.message)})).catch((e=>{console.error("Error:",e),msgbox("Image generation error: "+e)}))}function click_image(e){if(e){document.getElementById("zoomedimgcontainer").classList.remove("hidden"),document.getElementById("zoomedimg").src=e.src;let t=e.title;t&&""!=t?(t=replaceAll(t,"<br>"," "),document.getElementById("zoomedimgdesc").innerText=t):document.getElementById("zoomedimgdesc").innerText="No Saved Description"}}function delete_curr_image(){let e=document.getElementById("zoomedimg").src;if(e&&""!=e){var t="[<|d|"+e+"|d|>]";for(let e=0;e<gametext_arr.length;++e)if(gametext_arr[e].includes(t)){gametext_arr[e]=gametext_arr[e].replace(t,""),""==gametext_arr[e]&&gametext_arr.splice(e,1);break}render_gametext()}}function render_image_html(e,t="",n=!0){var o=2==localsettings.opmode?160:200;let r=n?"storyimgfloat":"storyimg",s="";if(e&&""!=e){let t=cyrb_hash(e);return null!=completed_imgs_meta[t]&&(s=completed_imgs_meta[t].alt?escapeHtml(completed_imgs_meta[t].alt):""),'<div class="'+r+'"><img src="'+e+'" width='+o+" height="+o+' title="'+s+'" style="border-radius: 6%; cursor: pointer;" onclick="return click_image(this);"></div>'}{let e="Unavailable";if(null!=image_db[t]){let n=image_db[t].queue;s=image_db[t].alt?escapeHtml(image_db[t].alt):"",e=0==n?"Generating":"Starting"==n?n:"Queue: "+n}else console.log("Cannot render "+t);return'<div class="'+r+'" contenteditable="false"><img 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width='+o+" height="+o+' style="border-radius: 6%;" title="'+s+'" alt="'+t+'"><div class="loader2"></div><div class="imagelabel">'+e+"</div></div>"}}function handle_incoming_text(e,t,n,o){if(""!=extrastopseq){let t=replaceAll(extrastopseq,"\\n","\n").split("||$||");if(t.length>0)for(let n=0;n<t.length;++n)if(t[n]&&""!=t[n]){let o=e.indexOf(t[n]);-1!=o&&(e=e.substr(0,o)+t[n])}}if((2==localsettings.opmode||3==localsettings.opmode&&!localsettings.allow_continue_chat||1==localsettings.trimsentences)&&(e=end_trim_to_sentence(e,!0)),2==localsettings.opmode){let t=[];-1!=e.indexOf("\n> ")&&(t=e.split("\n> "),e=t[0])}if(3==localsettings.opmode){let t=e.indexOf(localsettings.chatname+":"),n=e.indexOf("\n"+localsettings.chatname+" "),o=[];if(-1!=t)o=e.split(localsettings.chatname+":");else if(-1!=n)o=e.split("\n"+localsettings.chatname+" ");else if(localsettings.multiline_replies)o.push(e);else if(0==e.indexOf('"')&&e.indexOf('"',1)>0){let t=e.indexOf('"',1);o.push(e.substring(0,t+1))}else o=e.split("\n");let r=o[0];r.length>0&&"\n"==r[r.length-1]&&(r=r.substring(0,r.length-1)),e=r}if(4==localsettings.opmode){let t=get_instruct_starttag(!0),n=get_instruct_endtag(!0),o=e.indexOf(t),r=[];-1!=o&&(r=e.split(t),e=r[0]),o=e.indexOf(n),r=[],-1!=o&&(r=e.split(n),e=r[0])}if(""!=pending_context_preinjection&&(""!=e&&" "!=e[0]&&3==localsettings.opmode&&(e=" "+e),e=pending_context_preinjection+e,pending_context_preinjection=""),localsettings.speech_synth>0&&"speechSynthesis"in window){let t=new window.SpeechSynthesisUtterance(e);t.voice=window.speechSynthesis.getVoices()[localsettings.speech_synth-1],window.speechSynthesis.speak(t)}""!=e&&gametext_arr.push(e),localsettings.beep_on&&playbeep();let r='<a href="#" onclick="show_last_req()">Last request</a> served by <a href="#" onclick="get_and_show_workers()">'+t+'</a> using <span class="color_darkgreen">'+n+"</span> for "+o+" kudos in "+getTimeTaken()+" seconds.";document.getElementById("lastreq").innerHTML=r,document.getElementById("lastreq2").innerHTML=r}function poll_image_db(){console.log("polling for pending images "+Object.keys(image_db).length);for(let e in image_db){let t=image_db[e];0==t.done&&fetch(stablehorde_poll_endpoint+"/"+e).then((e=>e.json())).then((n=>{console.log("pollimg result:",n),1==n.faulted||0==n.is_possible?(msgbox("Pending image generation could not complete."),console.log("removing from images: "+e),delete image_db[e]):1==n.done?(t.done=!0,fetch(stablehorde_output_endpoint+"/"+e).then((e=>e.json())).then((n=>{if(console.log("finalimg recv for "+e),1==n.faulted||0==n.is_possible)msgbox("Pending image generation could not complete."),console.log("removing from images: "+e),delete image_db[e];else{t.queue=0,compressImage("data:image/jpeg;base64,"+n.generations[0].img,(e=>{t.result=e}))}})).catch((t=>{console.error("Error:",t),msgbox("Image poll error: "+t),delete image_db[e]}))):t.queue=null==n.queue_position?"Error":n.queue_position})).catch((t=>{console.error("Error:",t),msgbox("Image poll error: "+t),delete image_db[e]}))}let e=!1;for(var t=0;t<gametext_arr.length;++t)if(/\[<\|p\|.+?\|p\|>\]/.test(gametext_arr[t]))for(let n in image_db){let o=image_db[n],r="[<|p|"+n+"|p|>]";if(gametext_arr[t].includes(r)&&(e=!0,1==o.done&&""!=o.result)){let e="[<|d|"+o.result+"|d|>]";console.log("Replacing with Image: "+r),gametext_arr[t]=gametext_arr[t].replace(r,e),completed_imgs_meta[cyrb_hash(o.result)]={alt:image_db[n].alt},delete image_db[n]}}e&&document.activeElement!=document.getElementById("gametext")&&render_gametext()}function compressImage(e,t){let n=document.createElement("img");n.onload=function(){var e=document.createElement("canvas"),n=e.getContext("2d");e.width=256,e.height=256,n.drawImage(this,0,0,256,256);var o=e.toDataURL("image/jpeg",.33);t(o)},n.src=e}var idle_timer=0,idle_triggered_counter=0;function poll_background_tasks(){let e=1e3*localsettings.idle_duration,t=""==document.getElementById("input_text").value,n=""==document.getElementById("cht_inp").value;if((1==localsettings.opmode||3==localsettings.opmode)&&localsettings.idle_responses>0&&t&&n&&!document.getElementById("btnsend").disabled&&idle_triggered_counter<localsettings.idle_responses){if((idle_timer+=1e3)>e){idle_timer=0;let e=++idle_triggered_counter;submit_generation(),idle_triggered_counter=e}console.log("Idling: "+idle_timer+", "+idle_triggered_counter)}else idle_timer=0}function poll_pending_response(){if(++poll_ticks_passed,is_using_custom_ep()||poll_ticks_passed%3==0)if(show_abort_button(!1),pending_response_id&&"-1"!=pending_response_id&&""!=pending_response_id)if(poll_ticks_passed>4/(.001*poll_interval_base_text)&&show_abort_button(!0),poll_in_progress)console.log("Polling still in progress for id: "+pending_response_id);else if(is_using_custom_ep())if(poll_in_progress=!0,null==synchro_polled_response){console.log("v1 still awaiting reply");let e=should_use_pseudostreaming();!!!document.getElementById("pseudostreaming").checked||e||waiting_for_autosummary||poll_ticks_passed%2!=0?poll_in_progress=!1:fetch(custom_kobold_endpoint+koboldcpp_check_endpoint,{method:"POST",headers:{"Content-Type":"application/json"}}).then((e=>e.json())).then((e=>{e&&null!=e.results&&e.results.length>0&&e.results[0].text&&pending_response_id&&""!=pending_response_id&&(synchro_pending_stream=e.results[0].text,render_gametext()),poll_in_progress=!1})).catch((e=>{console.error("Error:",e),poll_in_progress=!1}))}else{console.log("v1 handle recv reply"),pending_response_id="",poll_in_progress=!1;let e=synchro_polled_response;if(null!=e&&""!=e){let t=e,n="Custom Endpoint",o="0",r=selected_models.length>0?selected_models[0].name:"Unknown Model";waiting_for_autosummary?handle_incoming_autosummary(t):handle_incoming_text(t,n,r,o)}synchro_polled_response=null,synchro_pending_stream="",show_abort_button(!1),render_gametext()}else console.log("v2 Polling started for pending id: "+pending_response_id),poll_in_progress=!0,fetch(pending_response_horde.polling_endpoint+"/"+pending_response_id).then((e=>e.json())).then((e=>{if(null!=e.message||1==e.faulted||0==e.is_possible){console.log("Gave up on failed attempt"),clear_poll_flags(),render_gametext(),show_abort_button(!1);let t="Error encountered during text generation!\n";null!=e.message&&(t+=e.message),1==e.faulted&&(t+="Fault encountered during text generation."),0==e.is_possible&&(t+="No workers were able to generate text with your request."),msgbox(t)}else if(1==e.done)setTimeout((()=>{console.log("fetching completed generation for "+pending_response_id),fetch(pending_response_horde.output_endpoint+"/"+pending_response_id).then((e=>e.json())).then((e=>{if(console.log("Finished "+pending_response_id+": "+JSON.stringify(e)),pending_response_id="",poll_in_progress=!1,null!=e.generations&&e.generations.length>0){let t=e.generations[0].text,n=e.generations[0].worker_name,o=e.generations[0].model,r=e.kudos;waiting_for_autosummary?handle_incoming_autosummary(t):handle_incoming_text(t,n,o,r)}render_gametext(),show_abort_button(!1)})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),show_abort_button(!1),msgbox("Error encountered during text generation!")}))}),500);else{poll_in_progress=!1;let t=document.getElementById("maintxtloader");if(t){t.classList.remove("greenloader"),t.classList.remove("redloader"),e.queue_position>0?t.classList.add("redloader"):1==e.processing&&0==e.queue_position&&t.classList.add("greenloader");let n=document.getElementById("outerloadernum");n&&(n.innerText=0==e.queue_position?"":e.queue_position)}console.log("Still awaiting "+pending_response_id+": "+JSON.stringify(e))}})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),show_abort_button(!1),msgbox("Error encountered during text generation!")}));else console.log("Nothing to update: "+pending_response_id)}function click_gametext(){if(document.getElementById("allowediting").checked){if(void 0!==window.getSelection){const e=window.getSelection();null!=e.focusNode&&null!=e.focusNode.parentElement&&e.focusNode.parentElement.classList.contains("txtchunk")&&(null!=prev_hl_chunk&&prev_hl_chunk.classList.remove("hlchunk"),(prev_hl_chunk=e.focusNode.parentElement).classList.add("hlchunk")),idle_timer=0}}}function merge_edit_field(){if(gametext_arr.length>0&&document.getElementById("allowediting").checked){let t=concat_gametext(!0,"\n","",""),n=document.getElementById("gametext");if(t!=n.innerText){gametext_arr=[],redo_arr=[],retry_prev_text="",redo_prev_text="",n.querySelectorAll("div.storyimg,div.storyimgfloat").forEach((e=>{let t=e.getElementsByTagName("img")[0];e.replaceWith(null==t.alt||""==t.alt?"[<|d|"+t.src+"|d|>]":"[<|p|"+t.alt+"|p|>]")}));let t=[];n.querySelectorAll("span.txtchunk").forEach((e=>{t.push(e.innerText)})),n.innerHTML=n.innerHTML.replace(/<span class="(.+?)">(.+?)<\/span>/g,"$2"),n.innerHTML=n.innerHTML.replace(/<span class="(.+?)">(.+?)<\/span>/g,"$2"),n.innerHTML=replaceAll(n.innerHTML,"<div><br><br><br></div>","<br><br><br>"),n.innerHTML=replaceAll(n.innerHTML,"<div><br><br></div>","<br><br>"),n.innerHTML=replaceAll(n.innerHTML,"<div><br></div>","<br>");let o=n.innerText,r="";if(t.length>1){let e=t[t.length-1].length;e>0&&(r=o.slice(-e),o=o.slice(0,-e))}if(o.length>0){let t="\n";o.includes("\n\n")&&(t="\n\n");let n=o.split(t);for(var e=0;e<n.length;++e)0!=e?gametext_arr.push(t+n[e]):gametext_arr.push(n[e])}""!=r&&(gametext_arr.length>0&&"\n"==gametext_arr[gametext_arr.length-1]?gametext_arr[gametext_arr.length-1]+=r:gametext_arr.push(r)),render_gametext(),console.log("Merged edit field. Parts:"+gametext_arr.length)}null!=prev_hl_chunk&&(prev_hl_chunk.classList.remove("hlchunk"),prev_hl_chunk=null)}}function concat_gametext(e=!1,t="",n="",o="",r=!1){let s="";for(let e=0;e<gametext_arr.length;++e){let t=gametext_arr[e];r&&(t=escapeHtml(t)),""==t.trim()||"\n"==t.trim()?s+=t:s+=n+t+o}if(r){if(s=s.replace(/\[&lt;\|p\|.+?\|p\|&gt;\]/g,(function(e){return unescapeHtml(e)})),s=s.replace(/\[&lt;\|d\|.+?\|d\|&gt;\]/g,(function(e){return unescapeHtml(e)})),s=s.replace(/\[&lt;\|.+?\|&gt;\]/g,(function(e){return unescapeHtml(e)})),s=s.replace(/\n\n&gt; /g,(function(e){return unescapeHtml(e)})),3==localsettings.opmode&&""!=localsettings.chatname&&""!=localsettings.chatopponent){s=replaceAll(s,escapeHtml(localsettings.chatname),localsettings.chatname);var l=new RegExp("\n(?!"+localsettings.chatname+").+?: ","gi");s=s.replace(l,(function(e){return unescapeHtml(e)}))}if(4==localsettings.opmode&&""!=localsettings.instruct_starttag&&""!=localsettings.instruct_endtag){let e=escapeHtml(get_instruct_starttag(!1)),t=escapeHtml(get_instruct_endtag(!1));s=replaceAll(s,e,get_instruct_starttag(!1)),s=replaceAll(s,t,get_instruct_endtag(!1))}}return e&&(s=s.replace(/\[<\|p\|.+?\|p\|>\]/g,t),s=s.replace(/\[<\|d\|.+?\|d\|>\]/g,t),s=s.replace(/\[<\|.+?\|>\]/g,"")),s}function migrate_old_images_in_gametext(){let e=concat_gametext(!1,"","","",!1);if(!/\[<\|p\|.+?\|p\|>\]/.test(e)&&!/\[<\|d\|.+?\|d\|>\]/.test(e)&&(/<\|p\|.+?\|p\|>/.test(e)||/<\|d\|.+?\|d\|>/.test(e))){console.log("Migrating old images from saved story");for(let e=0;e<gametext_arr.length;++e)gametext_arr[e]=gametext_arr[e].replace(/<\|p\|.+?\|p\|>/g,(function(e){return"["+e+"]"})),gametext_arr[e]=gametext_arr[e].replace(/<\|d\|.+?\|d\|>/g,(function(e){return"["+e+"]"}))}}function render_gametext(e=!1){if(document.getElementById("gametext").contentEditable=document.getElementById("allowediting").checked&&""==pending_response_id,2==localsettings.opmode?(document.getElementById("inputrow").classList.add("show_mode"),localsettings.adventure_is_action?(document.getElementById("adventure_mode_txt").innerText="Action",document.getElementById("adventure_mode_img").classList.add("input_action"),document.getElementById("adventure_mode_img").classList.remove("input_story")):(document.getElementById("adventure_mode_txt").innerText="Story",document.getElementById("adventure_mode_img").classList.remove("input_action"),document.getElementById("adventure_mode_img").classList.add("input_story")),document.getElementById("btnmode").classList.remove("hidden")):(document.getElementById("inputrow").classList.remove("show_mode"),document.getElementById("btnmode").classList.add("hidden")),0!=gametext_arr.length||""!=synchro_pending_stream&&""!=pending_response_id){let e="";if(e=document.getElementById("allowediting").checked?concat_gametext(!1,"",'<span class="txtchunk">',"</span>",!0):concat_gametext(!1,"","","",!0),""!=synchro_pending_stream&&(e+='<span class="color_yellow">'+escapeHtml(pending_context_preinjection)+escapeHtml(synchro_pending_stream)+"</span>"),4!=localsettings.opmode||document.getElementById("allowediting").checked)e=replaceAll(e,get_instruct_starttag(!0),'<span class="color_gray">'+escapeHtml(get_instruct_starttag(!0))+"</span>"),e=replaceAll(e,get_instruct_endtag(!0),'<span class="color_gray">'+escapeHtml(get_instruct_endtag(!0))+"</span>");else{if(e=replaceAll(e,"\n\n"+get_instruct_starttag(!0)+"\n\n","%SpcStg%"),e=replaceAll(e,"\n\n"+get_instruct_endtag(!0)+"\n\n","%SpcEtg%"),e=replaceAll(e,"\n"+get_instruct_starttag(!0)+"\n","%SpcStg%"),e=replaceAll(e,"\n"+get_instruct_endtag(!0)+"\n","%SpcEtg%"),e=replaceAll(e,get_instruct_starttag(!1),"%SpcStg%"),e=replaceAll(e,get_instruct_endtag(!1),"%SpcEtg%"),e=replaceAll(e,get_instruct_starttag(!0),"%SpcStg%"),e=replaceAll(e,get_instruct_endtag(!0),"%SpcEtg%"),localsettings.instruct_has_markdown&&""==synchro_pending_stream){e=e.replace(/(\n[-*] .+?)(%SpcStg%)/g,"$1\n$2");let t=(e.match(/```/g)||[]).length;t>0&&t%2!=0&&(e+="```"),e=simpleMarkdown(e)}e=replaceAll(e,"%SpcStg%",'<hr class="hr_instruct"><span class="color_cyan"><img src="'+human_square+'" style="padding:3px 6px 3px 3px;border-radius: 8%;"/>'),e=replaceAll(e,"%SpcEtg%",'</span><hr class="hr_instruct"><img src="'+niko_square+'" style="padding:3px 6px 3px 3px;border-radius: 8%;"/>')}if(3==localsettings.opmode){let n="\n"+localsettings.chatname+": ";var t=new RegExp("\n(?!"+localsettings.chatname+").+?: ","gi");let o={},r=0;e=e.replace(t,(function(e){let t=escapeHtml(e),n=t.trim();return null==o[n]&&(o[n]=GetUniqueColor(r),++r),'<span class="'+o[n]+'">'+t+"</span>"})),e=replaceAll(e,n,'<span class="color_blue">'+escapeHtml(n)+"</span>")}2==localsettings.opmode&&(e=e.replace(/\n\n\> .+?\n/g,(function(e){return'<span class="color_green">'+e+"</span>"}))),e=e.replace(/\[<\|p\|.+?\|p\|>\]/g,(function(e){let t=e.substring(5,e.length-5);return t=render_image_html("",t),t})),e=e.replace(/\[<\|d\|.+?\|d\|>\]/g,(function(e){let t=e.substring(5,e.length-5);return t=render_image_html(t,""),t})),e=e.replace(/(\r\n|\r|\n)/g,"<br>"),e.endsWith("<br>")&&!e.endsWith("<br><br>")&&(e=e.slice(0,-4)),document.getElementById("gametext").innerHTML=e}else{if(null==perfdata)document.getElementById("gametext").innerHTML='Welcome to <span class="color_cyan">KoboldAI Lite</span>!<br>You are in <span class="color_red">Offline Mode</span>.<br>You will still be able to load and edit stories, but not generate new text.';else{let e="";e=""!=custom_kobold_endpoint?'<br>You\'re using the custom KoboldAI endpoint at <span class="color_orange">'+custom_kobold_endpoint+"</span>":""!=custom_oai_key?"<br>You're using the OpenAI API":""!=custom_scale_key?"<br>You're using the Spellbook by Scale AI API":""!=custom_claude_key?"<br>You're using the Claude API":'<br>There are <span class="color_orange">'+selected_models.reduce(((e,t)=>e+t.count),0)+'</span> <a class="color_green" href="#" onclick="get_and_show_workers()">volunteer(s)</a> running selected models with a total queue length of <span class="color_orange">'+selected_models.reduce(((e,t)=>e+t.queued),0)+"</span> tokens",document.getElementById("gametext").innerHTML='Welcome to <span class="color_cyan">KoboldAI Lite</span>!<br>You are using the models <span class="color_green">'+selected_models.reduce(((e,t)=>e+(""==e?"":", ")+t.name),"")+"</span>"+(0==selected_workers.length?"":" (Pinned to "+selected_workers.length+" worker IDs)")+"."+e+'.<br><br>Enter a prompt below to begin!<br>Or, <a href="#" class="color_blueurl" onclick="display_scenarios()">select a Quick Start Scenario by clicking here.</a><br>'}document.getElementById("allowediting").checked&&(document.getElementById("allowediting").checked=!1,toggle_editable())}if(null==perfdata?(document.getElementById("topbtn_reconnect").classList.remove("hidden"),localmode?document.getElementById("topbtn_customendpt").classList.add("hidden"):document.getElementById("topbtn_customendpt").classList.remove("hidden"),document.getElementById("topbtn_ai").classList.add("hidden"),document.getElementById("topbtn_newgame").classList.remove("hidden"),document.getElementById("topbtn_save").classList.remove("hidden"),document.getElementById("topbtn_load").classList.remove("hidden"),document.getElementById("topbtn_settings").classList.add("hidden"),document.getElementById("topbtn_share").classList.add("hidden"),document.getElementById("topbtn_scenarios").classList.add("hidden"),document.getElementById("topbtn_quickplay").classList.add("hidden")):(document.getElementById("topbtn_reconnect").classList.add("hidden"),document.getElementById("topbtn_customendpt").classList.add("hidden"),localmode?document.getElementById("topbtn_ai").classList.add("hidden"):document.getElementById("topbtn_ai").classList.remove("hidden"),0==selected_models.length?(document.getElementById("topbtn_newgame").classList.add("hidden"),document.getElementById("topbtn_save").classList.add("hidden"),document.getElementById("topbtn_load").classList.add("hidden"),document.getElementById("topbtn_settings").classList.add("hidden"),document.getElementById("topbtn_share").classList.add("hidden"),document.getElementById("topbtn_scenarios").classList.add("hidden"),document.getElementById("topbtn_quickplay").classList.remove("hidden")):(document.getElementById("topbtn_newgame").classList.remove("hidden"),document.getElementById("topbtn_save").classList.remove("hidden"),document.getElementById("topbtn_load").classList.remove("hidden"),document.getElementById("topbtn_settings").classList.remove("hidden"),document.getElementById("topbtn_share").classList.remove("hidden"),document.getElementById("topbtn_scenarios").classList.remove("hidden"),document.getElementById("topbtn_quickplay").classList.add("hidden"))),0==selected_models.length?(document.getElementById("btn_actmem").disabled=!0,document.getElementById("btn_actwi").disabled=!0,document.getElementById("btn_actundo").disabled=!0,document.getElementById("btn_actredo").disabled=!0,document.getElementById("btn_actretry").disabled=!0,null==perfdata&&(document.getElementById("btn_actmem").disabled=!1,document.getElementById("btn_actwi").disabled=!1)):(document.getElementById("btn_actmem").disabled=!1,document.getElementById("btn_actwi").disabled=!1,document.getElementById("btn_actundo").disabled=!1,document.getElementById("btn_actredo").disabled=!1,document.getElementById("btn_actretry").disabled=!1),null==perfdata)document.getElementById("btnsend").disabled=!0,document.getElementById("btnsend").classList.add("wait"),document.getElementById("btnsend").classList.remove("btn-primary"),document.getElementById("btnsend").innerHTML="Offline",document.getElementById("fvico").href=favivon_normal;else if(0==selected_models.length&&0==selected_workers.length){document.getElementById("btnsend").disabled=!0,document.getElementById("btnsend").classList.add("wait"),document.getElementById("btnsend").classList.remove("btn-primary"),document.getElementById("btnsend").innerHTML="No AI<br>Loaded";let e='There are <span class="color_orange">'+perfdata.worker_count+'</span> total <a class="color_green" href="#" onclick="get_and_show_workers()">volunteer(s)</a> in the KoboldAI Horde, and <span class="color_orange">'+perfdata.queued_requests+'</span> request(s) in queues.<br>A total of <span class="color_orange">'+perfdata.past_minute_tokens+"</span> tokens were generated in the last minute.<br><br>";document.getElementById("gametext").innerHTML='Welcome to <span class="color_cyan">KoboldAI Lite</span>!<br><br>'+e+'<a href="#" class="color_blueurl" onclick="display_models()">Please select an AI model to use!</a><br>',document.getElementById("fvico").href=favivon_normal}else if(""==pending_response_id)document.getElementById("btnsend").disabled=!1,document.getElementById("btnsend").classList.remove("wait"),document.getElementById("btnsend").classList.add("btn-primary"),document.getElementById("btnsend").innerHTML="Submit",document.getElementById("fvico").href=favivon_normal;else{document.getElementById("btnsend").disabled=!0,document.getElementById("btnsend").classList.add("wait"),document.getElementById("btnsend").classList.remove("btn-primary");let e='<div class="outerloader"><div id="outerloadernum" class="outerloadernum"></div><div id="maintxtloader" class="innerloader"></div></div>';document.getElementById("btnsend").innerHTML!=e&&(document.getElementById("btnsend").innerHTML=e),document.getElementById("fvico").href=favicon_busy}let n=!!document.getElementById("allowediting").checked;localsettings.enhanced_chat_ui&&3==localsettings.opmode&&!n?(0==gametext_arr.length?render_enhanced_chat(document.getElementById("gametext").innerHTML):render_enhanced_chat(concat_gametext(!1,"","","",!0)),document.getElementById("enhancedchatinterface").classList.remove("hidden"),document.getElementById("normalinterface").classList.add("hidden")):(document.getElementById("enhancedchatinterface").classList.add("hidden"),document.getElementById("normalinterface").classList.remove("hidden")),document.getElementById("btnautogenmem").disabled=document.getElementById("btnsend").disabled,localsettings.persist_session&&autosave(),0==e&&localsettings.autoscroll&&(document.getElementById("gametext").scrollTop=document.getElementById("gametext").scrollHeight,document.getElementById("chat_msg_body").scrollTop=document.getElementById("chat_msg_body").scrollHeight),idle_timer=0,document.getElementById("token-budget").innerText=last_token_budget}function render_enhanced_chat(e){var t=document.getElementById("chat_msg_body");if(!t)return;let n="",o=!1;var r=new RegExp("(?!"+localsettings.chatname+").+?: ","gi"),s=new RegExp("\\|[d|p]\\|>(?!"+localsettings.chatname+").+?\\: ","gi");let l=[];e=(e=e.replace(s,(function(e){return e.substring(0,4)+"\n"+e.substring(4)}))).split("\n");localsettings.chatname;for(var a=new RegExp("("+localsettings.chatname+"): ","gi"),i=0;i<e.length;++i){let t=e[i];var c=t.match(r),d=t.match(a);null!=t&&(null!=d&&d.length>0?(o=!0,l.push({name:d[0].substring(0,d[0].length-2),msg:t.split(d[0])[1],myturn:o})):null!=c&&c.length>0?(o=!1,l.push({name:c[0].substring(0,c[0].length-2),msg:t.split(c[0])[1],myturn:o})):0==l.length?""!=t.trim()&&l.push({name:"",msg:t,myturn:o}):l[l.length-1].msg+="<br>"+t)}let m={},u=0;for(i=0;i<l.length;++i){let e=l[i];if(e.msg&&""!=e.msg&&(e.msg=e.msg.replace(/\[<\|p\|.+?\|p\|>\]/g,(function(e){let t=e.substring(5,e.length-5);return t=render_image_html("",t,!1),t})),e.msg=e.msg.replace(/\[<\|d\|.+?\|d\|>\]/g,(function(e){let t=e.substring(5,e.length-5);return t=render_image_html(t,"",!1),t})),e.msg=e.msg.replace(/\[<\|.+?\|>\]/g,""),e.msg=e.msg.replace(/\*(\S[^*]+\S)\*/g,"<em style='opacity:0.7'>$1</em>")),e.myturn){n+='<div class="chat_outgoing_msg"><div class="chat_sent_msg"><p>'+(""!=e.name?'<span style="font-weight: bolder;color:#15e4c8b9;">'+escapeHtml(e.name)+"</span><br>":"")+e.msg+"</p></div></div>"}else{let t=escapeHtml(e.name),o=t.trim();null==m[o]&&(m[o]=GetUniqueColor(u),++u),n+='<div class="incoming_msg"><div class="chat_received_msg"><div class="chat_received_withd_msg"><p>'+(""!=e.name?"<span class='"+m[o]+'\' style="font-weight: bolder;">'+t+"</span><br>":"")+e.msg+"</p></div></div></div>"}}""!=synchro_pending_stream&&(n+='<div class="incoming_msg"><div class="chat_received_msg"><div class="chat_received_withd_msg"><p><span 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n=["log","debug","warn","info"],o=0;o<n.length;o++)console[n[o]]=function(){}}console.log("Init started");try{let e=localStorage.getItem((localmode?"e_":"")+"kaihordewebui_settings"),t=localStorage.getItem((localmode?"e_":"")+"kaihordewebui_story");if(null!=e&&""!=e&&null!=t&&""!=t){let n=JSON.parse(e);n&&n.persist_session&&(import_share_story(t),import_props_into_object(localsettings,n),console.log("Loaded local settings and story")),n&&!n.persist_session&&(localsettings.persist_session=!1)}else console.log("Skipped missing local save")}catch(e){console.log("Discarded invalid local save: "+e)}if(localsettings.generate_images?(document.getElementById("btn_genimg").classList.remove("hidden"),document.getElementById("btn_genimg2").classList.remove("hidden")):(document.getElementById("btn_genimg").classList.add("hidden"),document.getElementById("btn_genimg2").classList.add("hidden")),toggle_invert_colors(),"speechSynthesis"in window){window.speechSynthesis.getVoices();console.log("Voices 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e="http://";window.location.protocol.includes("https")&&!is_using_web_lite()&&(e="https://"),document.getElementById("customendpoint").value=e+localmodehost+":"+localmodeport,connect_custom_endpoint(),document.getElementById("lastreq").innerHTML=document.getElementById("lastreq2").innerHTML="<span class=color_gray>You're using Kobold Lite Embedded.</span>"}else multifetch(perf_endpoints,((t,n)=>{if(t&&t.length>0){perfdata={queued_requests:0,queued_tokens:0,past_minute_tokens:0,worker_count:0};for(let e=0;e<t.length;++e){let n=t[e].data;n.hasOwnProperty("text_worker_count")?(perfdata.queued_requests+=n.queued_text_requests,perfdata.worker_count+=n.text_worker_count,perfdata.queued_tokens+=n.queued_tokens,perfdata.past_minute_tokens+=n.past_minute_tokens):(perfdata.queued_requests+=n.queued_requests,perfdata.worker_count+=n.worker_count,perfdata.queued_tokens+=n.queued_tokens,perfdata.past_minute_tokens+=n.past_minute_tokens)}document.body.classList.add("connected"),document.getElementById("connectstatus").innerHTML="Connected to KoboldAI Horde",document.getElementById("connectstatus").classList.remove("color_orange"),document.getElementById("connectstatus").classList.add("color_green"),render_gametext();const n=urlParams.get("s"),o=urlParams.get("scenario");if(urlParams.get("nofilter")&&(filter_enabled=!1,console.log("Safety filter is off. 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Mode",document.getElementById("connectstatus").classList.add("color_orange"),document.getElementById("connectstatus").classList.remove("color_green"),render_gametext()}));localflag||fetch_image_models(),initial_fetched_kudos||localsettings.my_api_key==defaultsettings.my_api_key||(document.getElementById("apikey").value=localsettings.my_api_key,initial_fetched_kudos=!0,fetch_kudo_balance())}var image_models_fetched=!1;function fetch_image_models(e){image_models_fetched||fetch(stablehorde_model_endpoint).then((e=>e.json())).then((t=>{image_models_fetched=!0,stablemodels=[],t=t.sort((function(e,t){return t.count-e.count}));for(var n=0;n<t.length;++n)stablemodels.push({name:t[n].name,count:t[n].count});console.log("Loaded SD models list: "+stablemodels.length),null!=e&&e()})).catch((e=>{console.log("Error: "+e)}))}function get_cursor_position(){let e=document.getElementById("gametext"),t=0;if(void 0!==window.getSelection){if(0!==window.getSelection().rangeCount){const n=window.getSelection().getRangeAt(0),o=n.cloneRange();o.selectNodeContents(e),o.setEnd(n.endContainer,n.endOffset),t=o.toString().length}}return t}function selectElementContents(e){var t=document.createRange();t.selectNodeContents(e);var n=window.getSelection();n.removeAllRanges(),n.addRange(t)}var timetaken_timestamp=performance.now();function startTimeTaken(){timetaken_timestamp=performance.now()}function getTimeTaken(){return((performance.now()-timetaken_timestamp)/1e3).toFixed(1)}function cyrb_hash(e,t=0){let n=3735928559^t,o=1103547991^t;for(let t,r=0;r<e.length;r++)t=e.charCodeAt(r),n=Math.imul(n^t,2654435761),o=Math.imul(o^t,1597334677);return n=Math.imul(n^n>>>16,2246822507)^Math.imul(o^o>>>13,3266489909),o=Math.imul(o^o>>>16,2246822507)^Math.imul(n^n>>>13,3266489909),(4294967296*(2097151&o)+(n>>>0)).toString(16).substring(0,6)}function import_props_into_object(e,t){for(var n in t)e[n]=t[n]}function is_using_custom_ep(){return""!=custom_oai_key||""!=custom_kobold_endpoint||""!=custom_scale_key||""!=custom_claude_key}function is_using_newer_kcpp(){return koboldcpp_version&&""!=koboldcpp_version&&compare_version_str(koboldcpp_version,"1.29")>0}function should_use_pseudostreaming(){let e=!!document.getElementById("pseudostreaming").checked,t=urlParams.get("streamamount");return is_using_newer_kcpp()&&(null==t||t<=0)&&(e=!1),waiting_for_autosummary&&(e=!1),e}function is_using_web_lite(){return window.location.hostname.includes("koboldai.net")||window.location.hostname.includes("kaihordewebui.github.io")}function get_most_common_cluster(e){let t=e[0].cluster,n={},o=0;for(let r=0;r<e.length;++r){let s=e[r].cluster;n[s]?n[s]++:n[s]=1,o<n[s]&&(t=s,o=n[s])}return t}function generate_compressed_story(e=!1){let t=gametext_arr;if(e){t=[];for(let e=0;e<gametext_arr.length;++e)t.push(gametext_arr[e].replace(/\[<\|p\|.+?\|p\|>\]/g,"").replace(/\[<\|d\|.+?\|d\|>\]/g,""))}for(var n={ga:t,md:[]},o=0;o<selected_models.length;++o)n.md.push(cyrb_hash(selected_models[o].name));""!=current_memory&&(n.cm=current_memory),""!=current_anote&&(n.ca=current_anote,n.ct=current_anotetemplate),""!=extrastopseq&&(n.ess=extrastopseq),null!=current_wi&&current_wi.length>0&&(n.cwi=current_wi),localsettings.export_settings&&(n.savedsettings=JSON.parse(JSON.stringify(localsettings)),n.savedsettings.my_api_key="0000000000",n.savedsettings.home_cluster=text_hordes[0].baseurl,n.savedsettings.saved_oai_key="",n.savedsettings.saved_oai_addr="",n.savedsettings.saved_claude_key="",n.savedsettings.saved_claude_addr="");var r=JSON.stringify(n);return console.log("Exporting story: "+r),buf_to_b64(lz_c.compress(r,1))}function export_share_story(){let e=generate_compressed_story(!0);console.log("Export Len: "+e.length),e.length>=4800?document.getElementById("sharewarning").classList.remove("hidden"):document.getElementById("sharewarning").classList.add("hidden"),document.getElementById("sharecontainer").classList.remove("hidden");let t="https://lite.koboldai.net/?s="+e;document.getElementById("sharestorytext").innerHTML='<a href="'+t+'">'+t+"</a>"}function copy_share_url(){var e=document.getElementById("sharestorytext");selectElementContents(e),navigator.clipboard.writeText(e.innerText)}function import_share_story(e){console.log("Importing shared story...");var t=!1,n=null;try{var o=lz_d.decompress(b64_to_buf(e));null==o||""==o?t=!0:n=JSON.parse(o)}catch(e){t=!0}if(null==n||t)msgbox("Could not import from URL. Is it valid?");else if(console.log("Importing story: "+o),fetch_models((e=>{if(0!=e.length||localmode){if(!localmode){selected_models=[];for(var t=0;t<e.length;++t)n.md.includes(cyrb_hash(e[t].name))&&selected_models.push(e[t]);0==selected_models.length&&selected_models.push(e[0]);if(!selected_models.every((e=>e.cluster===selected_models[0].cluster))){let e=get_most_common_cluster(selected_models);selected_models=selected_models.filter((t=>t.cluster===e))}render_gametext()}}else msgbox("No models available. Unable to load.")})),restart_new_game(),gametext_arr=n.ga,migrate_old_images_in_gametext(),n.ca&&""!=n.ca&&(current_anote=n.ca,current_anotetemplate=n.ct),n.cm&&""!=n.cm&&(current_memory=n.cm),n.cwi&&n.cwi.length>0&&(current_wi=n.cwi),n.ess&&""!=n.ess&&(extrastopseq=n.ess),n.savedsettings&&""!=n.savedsettings){let e=localsettings.my_api_key,t=localsettings.home_cluster,o=localsettings.saved_oai_key,r=localsettings.saved_oai_addr,s=localsettings.saved_claude_key,l=localsettings.saved_claude_addr;import_props_into_object(localsettings,n.savedsettings),localsettings.my_api_key=e,localsettings.home_cluster=t,localsettings.saved_oai_key=o,localsettings.saved_oai_addr=r,localsettings.saved_claude_key=s,localsettings.saved_claude_addr=l}}function generate_base_storyobj(){return{gamestarted:!0,prompt:"",memory:"",authorsnote:"",anotetemplate:"",actions:[],actions_metadata:{},worldinfo:[],wifolders_d:{},wifolders_l:[]}}var tempfileurl=null;let newfilename="";function savenowfn(){var e=document.getElementById("tempfile"),t=new Blob([JSON.stringify(loaded_storyobj)],{type:"application/json"});console.log("Normal save handling"),tempfileurl&&window.URL.revokeObjectURL(tempfileurl),tempfileurl=window.URL.createObjectURL(t),e.href=tempfileurl,e.target="_blank",e.download=newfilename,setTimeout((function(){e.click()}),20)}function save_file(){null==loaded_storyobj.file_version||(loaded_storyobj=generate_base_storyobj());let e=gametext_arr;if(!localsettings.save_images){e=[];for(let t=0;t<gametext_arr.length;++t)e.push(gametext_arr[t].replace(/\[<\|p\|.+?\|p\|>\]/g,"").replace(/\[<\|d\|.+?\|d\|>\]/g,""))}loaded_storyobj.prompt="",loaded_storyobj.actions=[],loaded_storyobj.actions_metadata={},e.length>0&&(loaded_storyobj.prompt=e[0]);for(var t=1;t<e.length;++t){loaded_storyobj.actions.push(e[t]);let n=(t-1).toString();loaded_storyobj.actions_metadata[n]={"Selected Text":e[t],"Alternative Text":[]}}loaded_storyobj.anotetemplate=current_anotetemplate,loaded_storyobj.authorsnote=current_anote,loaded_storyobj.memory=current_memory,loaded_storyobj.worldinfo=current_wi,loaded_storyobj.extrastopseq=extrastopseq,localsettings.export_settings?(loaded_storyobj.savedsettings=JSON.parse(JSON.stringify(localsettings)),loaded_storyobj.savedsettings.my_api_key="0000000000",loaded_storyobj.savedsettings.home_cluster=text_hordes[0].baseurl,loaded_storyobj.savedsettings.saved_oai_key="",loaded_storyobj.savedsettings.saved_oai_addr="",loaded_storyobj.savedsettings.saved_claude_key="",loaded_storyobj.savedsettings.saved_claude_addr=""):loaded_storyobj.savedsettings=null,window.URL=window.URL||window.webkitURL;var n=window.navigator.userAgent;if(newfilename=""==last_known_filename?"saved_story.json":last_known_filename,n.match(/AppleWebKit/)&&(n.match(/iPad/i)||n.match(/iPhone/i))){var o=new Blob([JSON.stringify(loaded_storyobj)],{type:"application/octet-stream"});console.log("Special save handling for iphones");var r=new FileReader;r.onload=function(e){msgbox('<button type="button" class="btn btn-primary" id="ios_save" onclick="savenowfn()">Click to Save</button><br><h4>If that does not work, right-click or long press <a href='+r.result+" target='_blank' download=\""+newfilename+'">this link</a>, and select (Save As)</h4>',"Save Story",!0)},r.readAsDataURL(o)}else savenowfn()}function load_file(e){let t=e.target;t.files.length>0?(load_selected_file(t.files[0]),document.getElementById("loadfileinput").value=""):console.log("No file to load")}function load_selected_file(e){var t="";e&&(t=e.name);let n=new FileReader;n.onload=function(){let o=n.result;console.log("Load file: "+o);try{let e=JSON.parse(o);null==e.prompt?null!=e.name||null!=e.description||null!=e.personality?load_tavern_obj(e):null!=e.char_name||null!=e.char_persona?load_ooba_obj(e):msgbox("Could not load selected json file. Does not appear to be a KoboldAI story or compatible format."):(kai_json_load(e),t&&""!=t&&(last_known_filename=t))}catch(n){console.log(n);var r=new FileReader;r.onload=function(){var e=r.result,n=new Uint8Array(e),s=convertTavernPng(n);if(null!=s)load_tavern_obj(s);else if(null!=(s=getTavernExifJSON(n)))load_tavern_obj(s);else{try{s=UnzipKAISTORYFile(n)}catch(e){console.log("Unzip failed: "+e),s=null}null!=s?kai_json_load(s):t.endsWith(".txt")?msgboxYesNo('Could not load selected file!<br><span class="color_red">It appears to be invalid or corrupted!</span><br><br>Do you still want to import it as plaintext?',"Loading Failed",(()=>{restart_new_game(),gametext_arr.push(o),hide_popups(),render_gametext()}),(()=>{hide_popups()}),!0):msgbox("Could not load selected file. Is it valid?")}},r.readAsArrayBuffer(e)}},n.readAsText(e)}function kai_json_load(e){restart_new_game();let t=null==(loaded_storyobj=e).file_version;if(console.log("Is oldui: "+t),t){""!=loaded_storyobj.prompt&&gametext_arr.push(loaded_storyobj.prompt);for(var n=0;n<loaded_storyobj.actions.length;++n)gametext_arr.push(loaded_storyobj.actions[n]);migrate_old_images_in_gametext(),loaded_storyobj.anotetemplate&&(current_anotetemplate=loaded_storyobj.anotetemplate),loaded_storyobj.authorsnote&&(current_anote=loaded_storyobj.authorsnote),loaded_storyobj.memory&&(current_memory=loaded_storyobj.memory),loaded_storyobj.worldinfo&&(current_wi=loaded_storyobj.worldinfo),loaded_storyobj.extrastopseq&&(extrastopseq=loaded_storyobj.extrastopseq),loaded_storyobj.savedsettings&&""!=loaded_storyobj.savedsettings&&msgboxYesNo("This story includes custom settings. Do you want to import them?","Import Story Settings",(()=>{let e=localsettings.my_api_key,t=localsettings.home_cluster,n=localsettings.saved_oai_key,o=localsettings.saved_oai_addr,r=localsettings.saved_claude_key,s=localsettings.saved_claude_addr;import_props_into_object(localsettings,loaded_storyobj.savedsettings),localsettings.my_api_key=e,localsettings.home_cluster=t,localsettings.saved_oai_key=n,localsettings.saved_oai_addr=o,localsettings.saved_claude_key=r,localsettings.saved_claude_addr=s,(1==localsettings.instruct_has_newlines||null!=loaded_storyobj.savedsettings&&null==loaded_storyobj.savedsettings.instruct_has_newlines&&null==loaded_storyobj.savedsettings.instruct_has_markdown)&&(localsettings.instruct_has_newlines=!1,localsettings.instruct_starttag.includes("\\n")||(localsettings.instruct_starttag="\\n"+localsettings.instruct_starttag+"\\n"),localsettings.instruct_endtag.includes("\\n")||(localsettings.instruct_endtag="\\n"+localsettings.instruct_endtag+"\\n")),hide_popups(),render_gametext()}),hide_popups)}else{for(var o in""!=loaded_storyobj.prompt&&gametext_arr.push(loaded_storyobj.prompt),loaded_storyobj.actions.actions){var r=loaded_storyobj.actions.actions[o];gametext_arr.push(r["Selected Text"])}if(loaded_storyobj.authornotetemplate&&(current_anotetemplate=loaded_storyobj.authornotetemplate),loaded_storyobj.authornote&&(current_anote=loaded_storyobj.authornote),loaded_storyobj.memory&&(current_memory=loaded_storyobj.memory),null!=loaded_storyobj.worldinfo_v2&&null!=loaded_storyobj.worldinfo_v2.entries)for(var o in loaded_storyobj.worldinfo_v2.entries){if((r=loaded_storyobj.worldinfo_v2.entries[o]).key.length>0&&null!=r.content){let e={key:r.key[0],keysecondary:r.keysecondary.length>0?r.keysecondary[0]:"",content:r.content,comment:r.comment,folder:null,selective:r.selective,constant:r.constant};current_wi.push(e)}}}render_gametext()}function load_tavern_wi(e,t,n){console.log("Append Tavern WI"),current_wi=[];for(let l in e.entries){var o=e.entries[l],r=o.key;r||(r=o.keys);var s=o.keysecondary;s||(s=o.secondary_keys);let a={key:r.join(","),keysecondary:s.length>0?s.join(","):"",content:o.content,comment:o.comment,folder:null,selective:o.selective,constant:o.constant};a.content=replaceAll(a.content,"{{char}}",t,!0),a.content=replaceAll(a.content,"{{user}}",n,!0),current_wi.push(a)}}function load_tavern_obj(e){console.log("Loading tavern obj"),"chara_card_v2"==e.spec&&null!=e.data&&(e=e.data);let t=e.name?e.name:defaultchatopponent,n=localsettings.chatname&&""!=localsettings.chatname?localsettings.chatname:"You",o=e.description?"Persona: "+e.description:"";o+=e.personality?"\nPersonality: "+e.personality:"";let r=e.scenario?e.scenario:"",s=e.mes_example?e.mes_example:"",l=e.first_mes?e.first_mes:"";o=replaceAll(o,"{{char}}",t,!0),r=replaceAll(r,"{{char}}",t,!0),l=replaceAll(l,"{{char}}",t,!0),s=replaceAll(s,"{{char}}",t,!0),o=replaceAll(o,"{{user}}",n,!0),r=replaceAll(r,"{{user}}",n,!0),l=replaceAll(l,"{{user}}",n,!0),s=replaceAll(s,"{{user}}",n,!0),""!=r&&(r="\n[Scenario: "+r+"]"),""!=s&&(s="\n"+s);let a=o+r+s;""==a.trim()&&""==l&&e.entries?load_tavern_wi(e,t,n):(restart_new_game(),localsettings.chatname=n,localsettings.chatopponent=t,gametext_arr.push("\n"+t+": "+l),current_memory=a+"\n<START>",localsettings.opmode=3,e.character_book&&e.character_book.entries&&e.character_book.entries.length>0&&load_tavern_wi(e.character_book,t,n)),render_gametext()}function load_ooba_obj(e){let t=e.char_name?e.char_name:defaultchatopponent,n=localsettings.chatname&&""!=localsettings.chatname?localsettings.chatname:"You",o=e.char_persona?"Persona: "+e.char_persona:"",r=e.world_scenario?e.world_scenario:"",s=e.example_dialogue?e.example_dialogue:"",l=e.char_greeting?e.char_greeting:"";o=replaceAll(o,"{{char}}",t),r=replaceAll(r,"{{char}}",t),l=replaceAll(l,"{{char}}",t),s=replaceAll(s,"{{char}}",t),o=replaceAll(o,"{{user}}",n),r=replaceAll(r,"{{user}}",n),l=replaceAll(l,"{{user}}",n),s=replaceAll(s,"{{user}}",n),""!=r&&(r="\n[Scenario: "+r+"]"),""!=s&&(s="\n"+s),restart_new_game(),localsettings.chatname=n,localsettings.chatopponent=t,gametext_arr.push("\n"+t+": "+l),current_memory=o+r+s+"\n<START>",localsettings.opmode=3,render_gametext()}function get_aetherroom_scenario(){inputBox("Enter aetherroom.club prompt URL, or 4-digit prompt number","Import from aetherroom.club","","https://aetherroom.club/1234",(()=>{let e=document.getElementById("inputboxcontainerinput").value.toLowerCase().trim();""==e||(e.includes("aetherroom.club/")&&(e=e.replace("/api/","/"),e=e.split("aetherroom.club/")[1],e=e.split("/")[0],e=e.split("#")[0],e=e.split("?")[0]),""!=e&&isNumeric(e)&&e>0&&e<5e4?fetch(cors_proxy+"?https://aetherroom.club/api/"+e).then((e=>e.json())).then((e=>{if(console.log(e),temp_scenario={title:e.title?e.title:"",desc:e.description?e.description:"",opmode:2,adventure_context_mod:!1,prefmodel1:["nerys","nerybus","skein","adventure","erebus"],prefmodel2:[],prompt:e.promptContent?e.promptContent:"",memory:e.memory?e.memory:"",authorsnote:e.authorsNote?e.authorsNote:"",worldinfo:[]},e.worldInfos)for(let t=0;t<e.worldInfos.length;++t){let n=e.worldInfos[t].keys,o=e.worldInfos[t].entry;temp_scenario.worldinfo.push({key:n||"",content:o||""})}preview_temp_scenario()})).catch((e=>{temp_scenario=null,document.getElementById("scenariodesc").innerText="Error: Selected scenario is invalid.",console.log("Error: "+e)})):(temp_scenario=null,document.getElementById("scenariodesc").innerText="Error: User input is invalid\n\n Please ensure you have input a valid aetherroom.club URL or ID (e.g. https://aetherroom.club/1234 or just 1234)"))}),!1)}function click_scenario(e){temp_scenario=scenario_db[e],preview_temp_scenario()}function preview_temp_scenario(){let e="";temp_scenario.author&&""!=temp_scenario.author&&(e="<br><b>Author:</b> "+temp_scenario.author),document.getElementById("scenariodesc").innerHTML="<p><b><u>"+escapeHtml(temp_scenario.title)+"</u></b></p><p><b>Mode:</b> "+(1==temp_scenario.opmode?"Story":2==temp_scenario.opmode?"Adventure":3==temp_scenario.opmode?"Chat":"Instruct")+e+"</p><p>"+(""!=temp_scenario.desc?escapeHtml(temp_scenario.desc):"[No Description Given]")+"</p>"}function complete_load_scenario(){if(console.log("Loading scenario..."),restart_new_game(),gametext_arr=[],""!=temp_scenario.prompt&&gametext_arr.push(temp_scenario.prompt),""!=temp_scenario.authorsnote&&(current_anote=temp_scenario.authorsnote),""!=temp_scenario.memory&&(current_memory=temp_scenario.memory),temp_scenario.worldinfo&&temp_scenario.worldinfo.length>0){current_wi=[];for(let e=0;e<temp_scenario.worldinfo.length;++e){let t=temp_scenario.worldinfo[e].key,n=temp_scenario.worldinfo[e].content,o={key:t||"",keysecondary:"",content:n||"",comment:"",folder:null,selective:!1,constant:!1};current_wi.push(o)}}localsettings.opmode=temp_scenario.opmode,3==temp_scenario.opmode&&(!0===temp_scenario.enhanced_chat_ui?localsettings.enhanced_chat_ui=!0:!1===temp_scenario.enhanced_chat_ui&&(localsettings.enhanced_chat_ui=!1),!0===temp_scenario.multiline_replies?localsettings.multiline_replies=!0:!1===temp_scenario.multiline_replies&&(localsettings.multiline_replies=!1),temp_scenario.chatopponent&&(localsettings.chatopponent=temp_scenario.chatopponent),temp_scenario.chatname&&(localsettings.chatname=temp_scenario.chatname)),2==temp_scenario.opmode&&(!0===temp_scenario.adventure_context_mod?localsettings.adventure_context_mod=!0:!1===temp_scenario.adventure_context_mod&&(localsettings.adventure_context_mod=!1),!0===temp_scenario.adventure_is_action?localsettings.adventure_is_action=!0:!1===temp_scenario.adventure_is_action&&(localsettings.adventure_is_action=!1)),4==temp_scenario.opmode&&(temp_scenario.enhanced_instruct_ui&&(localsettings.enhanced_instruct_ui=temp_scenario.enhanced_chat_ui),temp_scenario.instruct_starttag&&(localsettings.instruct_starttag=temp_scenario.instruct_starttag),temp_scenario.instruct_endtag&&(localsettings.instruct_endtag=temp_scenario.instruct_endtag)),render_gametext()}function togglescenarioallownsfw(){if(localmode)document.getElementById("scenarioautopickbox").classList.add("hidden");else{0==selected_models.length&&(document.getElementById("scenarioautopickai").checked=!0),!!document.getElementById("scenarioautopickai").checked?document.getElementById("scenarioallownsfwbox").classList.remove("hidden"):document.getElementById("scenarioallownsfwbox").classList.add("hidden")}}function confirm_scenario_verify(){if(1==temp_scenario.show_warning){inputBox("<p><b><u>Disclaimer: The AI is not suitable to be used as an actual therapist, counselor or advisor of any kind.</u></b></p>\n\t\t\t<p>While some find it comforting to talk about their issues with an AI, the responses are unpredictable.</p>\n\t\t\t<p>When using the AI for real world use-cases such as advice or counseling this means <b>you must be able to understand when an answer is wrong</b>.\n\t\t\tIf you would not trust a random person to pretend to be your advisor; you should definitely not use the AI for this. The models are simply too small and not trained for this purpose.</p>\n\t\t\t<p>If you still wish to proceed, please type the phrase I understand in the box below, exactly as written.</p>\n\t\t\t<p><b>If you are experiencing feelings of distress, anxiety, suicidal thoughts, or other forms of mental discomfort, it's best to avoid using AI for non fiction or personal matters as it may exacerbate or encourage these feelings.</b></p>\n\t\t\t","AI Safety Warning","","Acknowledgement Required",(()=>{"i understand"==document.getElementById("inputboxcontainerinput").value.toLowerCase().trim()&&confirm_scenario()}),!0)}else confirm_scenario()}function confirm_scenario(){if(null!=temp_scenario){hide_popups();let e=!!document.getElementById("scenarioautopickai").checked,t=!!document.getElementById("scenarioallownsfw").checked;0!=selected_models.length||is_using_custom_ep()||(e=!0),e&&!localmode?fetch_models((e=>{if(0==e.length)msgbox("No models available. Unable to load.");else{let s=["erebus","shinen","horni","litv2","lit-6b"];selected_models=[];for(var n=0;n<e.length;++n)for(var o=0;o<temp_scenario.prefmodel1.length;++o)if(e[n].name.trim().toLowerCase().includes(temp_scenario.prefmodel1[o].trim().toLowerCase())||temp_scenario.prefmodel1[o].trim().toLowerCase().includes(e[n].name.trim().toLowerCase())){let o=!0;if(!t)for(var r=0;r<s.length;++r)if(e[n].name.trim().toLowerCase().includes(s[r])){o=!1;break}o&&selected_models.push(e[n])}if(0==selected_models.length)for(n=0;n<e.length;++n)for(o=0;o<temp_scenario.prefmodel2.length;++o)if(e[n].name.trim().toLowerCase().includes(temp_scenario.prefmodel2[o].trim().toLowerCase())||temp_scenario.prefmodel2[o].trim().toLowerCase().includes(e[n].name.trim().toLowerCase())){let o=!0;if(!t)for(r=0;r<s.length;++r)if(e[n].name.trim().toLowerCase().includes(s[r])){o=!1;break}o&&selected_models.push(e[n])}0==selected_models.length&&selected_models.push(e[0]),complete_load_scenario(),temp_scenario=null}})):(complete_load_scenario(),temp_scenario=null)}}function display_scenarios(){temp_scenario=null,document.getElementById("quickstartcontainer").classList.remove("hidden");let e='<button type="button" name="" class="scenarioitem purple btn btn-primary" onclick="get_aetherroom_scenario()">Import from<br>aetherroom.club</button>';for(let t=0;t<scenario_db.length;++t){let n=scenario_db[t];e+='<button type="button" name="'+t+'" class="scenarioitem '+(1==n.opmode?"blue":2==n.opmode?"green":3==n.opmode?"red":"yellow")+' btn btn-primary" onclick="return click_scenario('+t+')">'+n.title+"</button>"}document.getElementById("scenariogrid").innerHTML=e,document.getElementById("scenariodesc").innerText="No Scenario Selected",togglescenarioallownsfw()}function scenario_search(){let e=document.getElementById("scenariogrid"),t=document.getElementById("scenariosearch").value.trim().toLowerCase(),n=document.getElementById("scenariosearchdropdown").value,o=e.children;for(let e=0;e<o.length;e++){let r=o[e],s=null;""!=r.name&&(s=scenario_db[r.name]),(""==t||r.innerText.trim().toLowerCase().includes(t))&&(0==n||s&&n==s.opmode)?r.style.display="block":r.style.display="none"}}function show_last_req(){msgbox(last_request_str,"Last Request Context",!1)}function get_and_show_workers(){localmode||get_workers((e=>{show_workers(e)}))}function get_workers(e){localmode?e([]):multifetch(worker_endpoints,((t,n)=>{if(t&&t.length>0){let n=[];for(let e=0;e<t.length;++e){let o=t[e].data;if(o)for(let r=0;r<o.length;++r){let s=o[r];s.cluster=t[e].cluster,s.hasOwnProperty("max_content_length")&&(s.max_context_length=s.max_content_length),n.push(s)}}null!=e&&e(n)}else console.log("Error: "+n),msgbox("Failed to connect to Worker Endpoint!\nPlease check your network connection.")}))}function show_workers(e){document.getElementById("workercontainer").classList.remove("hidden");let t="";for(var n=0;n<e.length;++n){let o=e[n],r=o.performance.replace(" tokens per second","");"no requests fulfilled yet"==r.toLowerCase()&&(r=0);let s=find_text_horde(o.cluster),l=s&&""!=s.tag?" "+s.tag:"",a=o.trusted?'style="color:#dd77ff;"':"",i=o.maintenance_mode?'style="color:#ee4444;"':"",c=escapeHtml(o.name.substring(0,32));o.info&&""!=o.info&&(c='<a class="color_blueurl" href="#" onclick="msgbox(\''+escapeHtml(replaceAll(o.info,"'","\\'"))+"','Worker Info',false,false,hide_msgbox)\">"+c+"</a>"),t+="<tr><td>"+c+"</td><td>"+escapeHtml(o.models[0].substring(0,32))+"</td><td>"+o.max_length+" / "+o.max_context_length+"<br>("+r+" T/s)</td><td "+i+">"+o.uptime+"<br>("+o.requests_fulfilled+" jobs)</td><td "+a+">"+o.kudos_rewards.toFixed(0)+"</td><td>"+l+"</td></tr>"}document.getElementById("workertable").innerHTML=t,document.getElementById("worktitlecount").innerText="Worker List - Total "+e.length}function show_my_own_workers(){let e=lastValidFoundUserData,t=find_text_horde(lastValidFoundCluster);if(lastValidFoundUserWorkers=[],t&&e&&e.worker_ids&&e.worker_ids.length>0){let n=e.worker_ids.map((e=>t.maintenance_endpoint+"/"+e));Promise.all(n.map((e=>fetch(e).then((e=>e.json()))))).then((e=>{lastValidFoundUserWorkers=e,console.log(e),document.getElementById("myownworkercontainer").classList.remove("hidden");let t="";for(var n=0;n<e.length;++n){let o=e[n],r=o.trusted?'style="color:#dd77ff;"':"",s=o.maintenance_mode?'style="color:#ee4444;"':"";t+="<tr><td>"+escapeHtml(o.name.substring(0,32))+"</td><td><input class='' style='color:#000000;' id='mwc_desc_"+n+"' placeholder='Worker Description' value='"+(o.info&&""!=o.info?o.info:"")+"''></td><td "+s+">"+o.uptime+"<br>("+o.requests_fulfilled+" jobs)</td><td "+r+">"+o.kudos_rewards.toFixed(0)+"</td><td>"+(o.online?"Online":"Offline")+"</td><td><input type='checkbox' id='mwc_maint_"+n+"' "+(o.maintenance_mode?"checked":"")+"></td></tr>"}document.getElementById("myownworkertable").innerHTML=t,localsettings.my_api_key=document.getElementById("apikey").value,null!=localsettings.my_api_key&&""!=localsettings.my_api_key||(localsettings.my_api_key=defaultsettings.my_api_key),autosave()})).catch((e=>{console.log("Error: "+e),msgbox(e,"Error fetching my workers")}))}else msgbox("Unable to find my horde workers.","No valid workers found")}function hide_workertable(){document.getElementById("workercontainer").classList.add("hidden"),document.getElementById("myownworkercontainer").classList.add("hidden")}function hide_popups(){document.getElementById("loadmodelcontainer").classList.add("hidden"),document.getElementById("newgamecontainer").classList.add("hidden"),document.getElementById("yesnocontainer").classList.add("hidden"),document.getElementById("settingscontainer").classList.add("hidden"),document.getElementById("msgboxcontainer").classList.add("hidden"),document.getElementById("memorycontainer").classList.add("hidden"),document.getElementById("workercontainer").classList.add("hidden"),document.getElementById("myownworkercontainer").classList.add("hidden"),document.getElementById("sharecontainer").classList.add("hidden"),document.getElementById("wicontainer").classList.add("hidden"),document.getElementById("customendpointcontainer").classList.add("hidden"),document.getElementById("quickstartcontainer").classList.add("hidden"),document.getElementById("zoomedimgcontainer").classList.add("hidden")}function explain_horde(){msgbox('The AI Horde generates text using crowdsourced GPUs by volunteer workers. By default your inputs are not logged, but as Horde workers are open source, they can be modified to do so. <br><br>In all cases, the sender will *always be anonymous*, however you are still advised to avoid sending privacy sensitive information.<br><br>For any issues, you can find us on discord at <a class="color_blueurl" href="https://koboldai.org/discord">https://koboldai.org/discord</a>',"Disclaimer",!0)}var pendingstyle="";function selectStyle(){inputBox("Style tags to use for generating images:\n(E.g. Sketch, Realistic, Anime, 3D Render, Drawing)\n\n","Extra Image Styles",pendingstyle,"Default Style",(()=>{let e=document.getElementById("inputboxcontainerinput").value;pendingstyle=e,console.log("Saved styles: "+pendingstyle)}),!1)}var msgboxOnDone=hide_popups;function hide_msgbox(){document.getElementById("msgboxcontainer").classList.add("hidden")}function msgbox(e,t="Error Encountered",n=!1,o=!1,r=hide_popups){e||(e=""),n?document.getElementById("msgboxtxt").innerHTML=e:document.getElementById("msgboxtxt").innerText=e,document.getElementById("msgboxtitle").innerText=t,document.getElementById("msgboxcontainer").classList.remove("hidden"),1==o?document.getElementById("msgboxbtnok").classList.add("hidden"):document.getElementById("msgboxbtnok").classList.remove("hidden"),msgboxOnDone=r,console.log("Msgbox: "+e)}var onYesFn=null,onNoFn=null;function msgboxYesNo(e,t,n,o,r=!1){e||(e=""),document.getElementById("yesnocontainer").classList.remove("hidden"),document.getElementById("yesnocontainertitle").innerText=t,r?document.getElementById("yesnocontainertext").innerHTML=e:document.getElementById("yesnocontainertext").innerText=e,onYesFn=n,onNoFn=o}var onInputboxOk=null;function inputBox(e,t,n,o,r,s=!1){e||(e=""),t||(t="User Input"),document.getElementById("inputboxcontainer").classList.remove("hidden"),document.getElementById("inputboxcontainertitle").innerText=t,s?document.getElementById("inputboxcontainertext").innerHTML=e:document.getElementById("inputboxcontainertext").innerText=e,document.getElementById("inputboxcontainerinput").value=escapeHtml(n),document.getElementById("inputboxcontainerinput").placeholder=escapeHtml(o),onInputboxOk=function(){document.getElementById("inputboxcontainer").classList.add("hidden"),r()}}function togglejailbreak(){document.getElementById("jailbreakprompt").checked?document.getElementById("jailbreakprompttext").classList.remove("hidden"):document.getElementById("jailbreakprompttext").classList.add("hidden")}function customapi_dropdown(){let e=document.getElementById("customapidropdown").value;document.getElementById("oaicustom").classList.add("hidden"),document.getElementById("koboldcustom").classList.add("hidden"),document.getElementById("scalecustom").classList.add("hidden"),document.getElementById("claudecustom").classList.add("hidden"),0==e?document.getElementById("koboldcustom").classList.remove("hidden"):1==e?(document.getElementById("oaicustom").classList.remove("hidden"),document.getElementById("custom_oai_endpoint").value=custom_oai_endpoint,""==custom_oai_key&&""!=localsettings.saved_oai_key&&(document.getElementById("custom_oai_key").value=localsettings.saved_oai_key,""!=localsettings.saved_oai_addr&&(document.getElementById("custom_oai_endpoint").value=localsettings.saved_oai_addr)),togglejailbreak()):2==e?document.getElementById("scalecustom").classList.remove("hidden"):3==e&&(document.getElementById("claudecustom").classList.remove("hidden"),document.getElementById("custom_claude_endpoint").value=custom_claude_endpoint,""==custom_claude_key&&""!=localsettings.saved_claude_key&&(document.getElementById("custom_claude_key").value=localsettings.saved_claude_key,""!=localsettings.saved_claude_addr&&(document.getElementById("custom_claude_endpoint").value=localsettings.saved_claude_addr)))}function connect_custom_endpoint(){custom_kobold_endpoint="",custom_oai_key="",custom_scale_key="";let e=document.getElementById("customapidropdown").value;if(0==e){let e=document.getElementById("customendpoint").value;if(null!=e&&""!=e.trim()){hide_popups(),e=e.trim(),e=e.endsWith("#")?e.slice(0,-1):e,e=e.endsWith("/")?e.slice(0,-1):e;let t=[apply_proxy_url(e+kobold_custom_mdl_endpoint),apply_proxy_url(e+kobold_custom_version_endpoint)];Promise.all(t.map((e=>fetch(e).then((e=>e.json()))))).then((t=>{console.log(t);let n=t[0].result,o=t[1].result;n?"ReadOnly"==n?(msgbox("The custom endpoint is working, but no model was loaded.\n\nPlease select and load a model and try again."),selected_models=[],selected_workers=[],custom_kobold_endpoint="",render_gametext()):(custom_kobold_endpoint=e,kobold_endpoint_version=o||"",selected_models=[{performance:100,queued:0,eta:0,name:n,count:1}],selected_workers=[],null==perfdata&&(perfdata={queued_requests:0,queued_tokens:0,past_minute_tokens:0,worker_count:0},document.body.classList.add("connected"),document.getElementById("connectstatus").classList.remove("color_orange"),document.getElementById("connectstatus").classList.add("color_green")),document.getElementById("connectstatus").innerHTML="Connected to Custom Endpoint",render_gametext(),localflag&&fetch(e+koboldcpp_version_endpoint).then((e=>e.json())).then((e=>{e&&""!=e&&e.version&&""!=e.version&&(koboldcpp_version=e.version,console.log("KoboldCpp Detected: "+koboldcpp_version))})).catch((e=>{console.log("Not using KoboldCpp")}))):(msgbox("Error at Custom Kobold Endpoint!\n\nThe custom endpoint failed to respond correctly."),selected_models=[],selected_workers=[],custom_kobold_endpoint="",render_gametext())})).catch((t=>{console.log("Error: "+t);let n=custom_kobold_endpoint.toLowerCase().includes("localhost")||custom_kobold_endpoint.toLowerCase().includes("127.0.0.1")||custom_kobold_endpoint.toLowerCase().includes("192.168.");uses_cors_proxy||n?(msgbox("Failed to connect to Custom Kobold Endpoint!\n\nPlease check if KoboldAI is running at the url: "+e),selected_models=[],selected_workers=[],custom_kobold_endpoint="",render_gametext()):(uses_cors_proxy=!0,connect_custom_endpoint())}))}}else if(1==e){let e=document.getElementById("custom_oai_key").value.trim(),t=document.getElementById("custom_oai_endpoint").value.trim();""==t&&(t=document.getElementById("custom_oai_endpoint").value=default_oai_base),""!=t&&"/"==t.slice(-1)&&(t=t.slice(0,-1)),""!=t&&t.length>4&&!t.slice(-4).toLowerCase().includes("/v")&&(t+="/v1"),""!=e&&""!=t&&(hide_popups(),fetch(t+oai_models_endpoint,{method:"GET",headers:{Authorization:"Bearer "+e,"x-api-key":e},referrerPolicy:"no-referrer"}).then((e=>e.json())).then((n=>{console.log(n),!n.error&&n.data&&n.data.length>0?(custom_oai_endpoint=t,custom_oai_key=e,localsettings.saved_oai_key=custom_oai_key,localsettings.saved_oai_addr=custom_oai_endpoint,custom_oai_model=document.getElementById("custom_oai_model").value.trim(),selected_models=[{performance:100,queued:0,eta:0,name:custom_oai_model,count:1}],selected_workers=[],null==perfdata&&(perfdata={queued_requests:0,queued_tokens:0,past_minute_tokens:0,worker_count:0},document.body.classList.add("connected"),document.getElementById("connectstatus").classList.remove("color_orange"),document.getElementById("connectstatus").classList.add("color_green")),document.getElementById("connectstatus").innerHTML="Connected to OAI Endpoint",render_gametext()):(custom_oai_endpoint=default_oai_base,custom_oai_key="",msgbox(JSON.stringify(n.error.message)))})).catch((e=>{console.log("Error: "+e),custom_oai_endpoint=default_oai_base,custom_oai_key="",msgbox("Error: "+e)})))}else if(2==e){let e=document.getElementById("custom_scale_key").value.trim(),t=document.getElementById("custom_scale_ID").value.trim();t=t.split("#")[0],t=t.split("?")[0],!t.includes("dashboard.scale.com/spellbook/api/v2/deploy/")||25!=e.length||e.includes(" ")||e.includes("/")?(t="",e="",msgbox("Invalid inputs, please try again.")):t=t.split("dashboard.scale.com/spellbook/api/v2/deploy/")[1],""!=e&&""!=t&&(hide_popups(),fetch(cors_proxy+"?"+scale_submit_endpoint+t,{method:"GET",headers:{Authorization:"Bearer "+e},referrerPolicy:"no-referrer"}).then((e=>e.json())).then((n=>{console.log(n),n.message&&""!=n.message?(custom_scale_key=e,custom_scale_ID=t,selected_models=[{performance:100,queued:0,eta:0,name:"SpellbookScaleAI",count:1}],selected_workers=[],null==perfdata&&(perfdata={queued_requests:0,queued_tokens:0,past_minute_tokens:0,worker_count:0},document.body.classList.add("connected"),document.getElementById("connectstatus").classList.remove("color_orange"),document.getElementById("connectstatus").classList.add("color_green")),document.getElementById("connectstatus").innerHTML="Connected to ScaleAI Endpoint",render_gametext()):(custom_scale_key="",msgbox("Cannot connect to Spellbook by ScaleAI"))})).catch((e=>{console.log("Error: "+e),custom_scale_key="",msgbox("Error: "+e)})))}else if(3==e){let e=document.getElementById("custom_claude_key").value.trim(),t=document.getElementById("custom_claude_endpoint").value.trim();""==t&&(t=document.getElementById("custom_claude_endpoint").value=default_claude_base),""!=t&&"/"==t.slice(-1)&&(t=t.slice(0,-1)),""!=t&&t.length>4&&!t.slice(-4).toLowerCase().includes("/v")&&(t+="/v1"),""!=e&&""!=t&&(hide_popups(),custom_claude_endpoint=t,custom_claude_key=e,localsettings.saved_claude_key=custom_claude_key,localsettings.saved_claude_addr=custom_claude_endpoint,custom_claude_model=document.getElementById("custom_claude_model").value.trim(),document.getElementById("clauderenamecompat").checked&&(localsettings.instruct_starttag="Human:",localsettings.chatname="Human",localsettings.instruct_endtag="Assistant:",localsettings.chatopponent="Assistant"),selected_models=[{performance:100,queued:0,eta:0,name:custom_claude_model,count:1}],selected_workers=[],null==perfdata&&(perfdata={queued_requests:0,queued_tokens:0,past_minute_tokens:0,worker_count:0},document.body.classList.add("connected"),document.getElementById("connectstatus").classList.remove("color_orange"),document.getElementById("connectstatus").classList.add("color_green")),document.getElementById("connectstatus").innerHTML="Connected to Claude Endpoint",render_gametext())}}function display_custom_endpoint(){document.getElementById("customendpointcontainer").classList.remove("hidden")}function fetch_models(e){localmode?e(selected_models):multifetch(models_endpoints,((t,n)=>{if(t&&t.length>0){let n=[];for(let e=0;e<t.length;++e){let o=t[e].data;if(o)for(let r=0;r<o.length;++r){let s=o[r];s.cluster=t[e].cluster,n.push(s)}}e(n)}else console.log("Error: "+n),msgbox("Failed to fetch models!\nPlease check your network connection.")}))}function display_models(){document.getElementById("pickedmodel").innerHTML="",document.getElementById("loadmodelcontainer").classList.remove("hidden"),document.getElementById("apikey").value=localsettings.my_api_key;let e=!!document.getElementById("manualworker").checked,t=!1,n=!1,o=!1;function r(){if(!o)if(o=!0,e){let e="";for(let t=0;t<worker_data.length;++t){let n=worker_data[t],o=n.models&&n.models.length>0?n.models[0]:"None",r=n.name,s=find_text_horde(n.cluster),l=s&&""!=s.tag?s.tag+" ":"",a=n.trusted?'style="color:#b700ff;"':"";a=n.maintenance_mode?'style="color:#ee4444;"':a;let i=n.trusted?" 💜":"";i=n.maintenance_mode?" ⛔":i,e+="<option "+a+' value="'+t+'" '+(selected_workers.filter((e=>e.cluster==n.cluster&&e.name==n.name)).length>0?" selected":"")+">"+l+escapeHtml(r)+" ("+escapeHtml(o)+")"+i+"</option>"}document.getElementById("pickedmodel").innerHTML=e}else{let e="";for(let t=0;t<models_data.length;++t){let n=models_data[t],o=find_text_horde(n.cluster),r=o&&""!=o.tag?o.tag+" ":"",s=selected_models.filter((e=>e.cluster==n.cluster&&e.name==n.name)).length>0?" selected":"",l=parseFloat(n.performance);if(!l||isNaN(l)||l>=99999){let e=worker_data.filter((e=>e.cluster==n.cluster&&e.models.includes(n.name)));if(e.length>0){l=0;for(let t=0;t<e.length;++t){let n=e[t].performance.replace(" tokens per second","");"no requests fulfilled yet"==n.toLowerCase()&&(n=0),l+=parseFloat(n)}l/=1*e.length,l=l.toFixed(1)}}e+='<option value="'+t+'" '+s+">"+r+escapeHtml(n.name)+" (ETA: "+n.eta+"s, Queue: "+n.queued+", Speed: "+l+", Qty: "+n.count+")</option>"}e+='<option style="color:#dd7723;font-weight:bold;" value="9999">📡 [ Remote Play / Custom API Endpoint ]</option>',document.getElementById("pickedmodel").innerHTML=e}}fetch_models((e=>{models_data=e,t=!0,t&&n&&r()})),get_workers((e=>{worker_data=e,n=!0,t&&n&&r()}))}function confirm_models(){let e=Array.from(document.getElementById("pickedmodel").selectedOptions).map((({value:e})=>e));if(1==e.length&&9999==e[0])hide_popups(),display_custom_endpoint();else{custom_kobold_endpoint="",custom_oai_key="",custom_scale_key="";const o=e.indexOf("9999");if(o>-1&&e.splice(o,1),e.length>0){let o=[],r=[],s=!!document.getElementById("manualworker").checked;for(var t=0;t<e.length;++t)if(s){let s=worker_data[e[t]];r.push(s);let l=s.models;for(var n=0;n<l.length;++n){let e=models_data.find((e=>e.name==l[n]&&e.cluster==s.cluster));o.includes(e)||o.push(e)}}else{let n=models_data[e[t]];o.push(n)}o=o.filter((e=>e)),r=r.filter((e=>e));const l=o.every((e=>e.cluster===o[0].cluster)),a=r.every((e=>e.cluster===r[0].cluster));if(!l||!a)if(r.length>0){let e=get_most_common_cluster(r);r=r.filter((t=>t.cluster===e)),o=o.filter((t=>t.cluster===e))}else{let e=get_most_common_cluster(o);o=o.filter((t=>t.cluster===e))}selected_models=o,selected_workers=r,localsettings.my_api_key=document.getElementById("apikey").value,null!=localsettings.my_api_key&&""!=localsettings.my_api_key||(localsettings.my_api_key=defaultsettings.my_api_key),null!=desired_new_home_cluster&&(localsettings.home_cluster=desired_new_home_cluster,desired_new_home_cluster=null),document.getElementById("connectstatus").innerHTML="Connected to KoboldAI Horde",render_gametext(),hide_popups(),l&&a||msgbox("You've selected multiple workers from different clusters. Only one cluster will be used.","Caution")}}}function update_my_workers(){let e=document.getElementById("apikey").value,t=find_text_horde(lastValidFoundCluster);for(var n=0;n<lastValidFoundUserWorkers.length;++n){let o=document.getElementById("mwc_desc_"+n),r=document.getElementById("mwc_maint_"+n);if(o&&r&&(""!=o.value.trim()&&(null==lastValidFoundUserWorkers[n].info||lastValidFoundUserWorkers[n].info!=o.value)||""==o.value.trim()&&null!=lastValidFoundUserWorkers[n].info&&""!=lastValidFoundUserWorkers[n].info||r.checked!=lastValidFoundUserWorkers[n].maintenance_mode)){console.log("updating worker "+lastValidFoundUserWorkers[n].id);let s={maintenance:r.checked};(""!=o.value.trim()||""==o.value.trim()&&null!=lastValidFoundUserWorkers[n].info&&""!=lastValidFoundUserWorkers[n].info)&&(s.info=o.value.trim()),fetch(t.maintenance_endpoint+"/"+lastValidFoundUserWorkers[n].id,{method:"PUT",headers:{"Content-Type":"application/json",apikey:e},body:JSON.stringify(s)}).then((e=>e.json())).then((e=>{msgbox(JSON.stringify(e),"Update My Worker")})).catch((e=>{console.error("Error:",e)}))}}}let desired_new_home_cluster=null,lastValidFoundUserData=null,lastValidFoundCluster=null,lastValidFoundUserWorkers=[];function fetch_kudo_balance(){if(localmode)return;desired_new_home_cluster=null;let e=document.getElementById("apikey").value;if(null!=e&&""!=e.trim()){document.getElementById("kudos_bal").innerHTML="Checking...<br>&nbsp;";let t={method:"GET",headers:{apikey:e}};multifetch(finduser_endpoints.map((e=>[e,t])),((e,t)=>{if(e&&e.length>0){lastValidFoundUserData=null,lastValidFoundCluster="";for(let t=0;t<e.length;++t){let n=e[t].data,o=e[t].cluster;if(n){let e=n.username;if(console.log(n),null!=e&&""!=e){lastValidFoundUserData=n,lastValidFoundCluster=o;break}}}if(lastValidFoundUserData){desired_new_home_cluster=lastValidFoundCluster;let e=lastValidFoundUserData.kudos,t=lastValidFoundUserData.username,n=find_text_horde(desired_new_home_cluster),o=n&&""!=n.tag?n.tag+" ":"",r="<a class='color_blueurl' href='#' onclick='show_my_own_workers()'>"+t+"</a>";e<0?(document.getElementById("kudos_bal").innerHTML=o+r+"<br>Kudos Balance: 0","anonymous#0"==t.toLowerCase()&&(document.getElementById("kudos_bal").innerHTML=o+t+"<br><a class='color_blueurl' href='https://horde.koboldai.net/register'>(Register New User)</a>")):document.getElementById("kudos_bal").innerHTML=o+r+"<br>Kudos Balance: "+e}else document.getElementById("kudos_bal").innerHTML="API Key Error<br><a class='color_blueurl' href='https://horde.koboldai.net/register'>(Register New User)</a>"}else console.log("Error: "+t),document.getElementById("kudos_bal").innerHTML="API Key Error<br><a class='color_blueurl' href='https://horde.koboldai.net/register'>(Register New User)</a>"}))}}function focus_api_keys(){var e=document.getElementById("apikey");e&&"password"===e.type&&(e.type="text"),(e=document.getElementById("custom_oai_key"))&&"password"===e.type&&(e.type="text"),(e=document.getElementById("custom_claude_key"))&&"password"===e.type&&(e.type="text")}function blur_api_keys(){var e=document.getElementById("apikey");e&&"text"===e.type&&(e.type="password"),(e=document.getElementById("custom_oai_key"))&&"text"===e.type&&(e.type="password"),(e=document.getElementById("custom_claude_key"))&&"text"===e.type&&(e.type="password")}function display_settings(){document.getElementById("settingscontainer").classList.remove("hidden"),document.getElementById("max_context_length").value=document.getElementById("max_context_length_slide").value=localsettings.max_context_length,document.getElementById("max_length").value=document.getElementById("max_length_slide").value=localsettings.max_length,document.getElementById("temperature").value=document.getElementById("temperature_slide").value=localsettings.temperature,document.getElementById("rep_pen").value=document.getElementById("rep_pen_slide").value=localsettings.rep_pen,document.getElementById("rep_pen_slope").value=localsettings.rep_pen_slope,document.getElementById("rep_pen_range").value=localsettings.rep_pen_range,document.getElementById("top_p").value=document.getElementById("top_p_slide").value=localsettings.top_p,document.getElementById("autoscroll").checked=localsettings.autoscroll,document.getElementById("export_settings").checked=localsettings.export_settings,document.getElementById("invert_colors").checked=localsettings.invert_colors,document.getElementById("trimsentences").checked=localsettings.trimsentences,document.getElementById("trimwhitespace").checked=localsettings.trimwhitespace,document.getElementById("persist_session").checked=localsettings.persist_session,document.getElementById("opmode").value=localsettings.opmode,document.getElementById("chatname").value=localsettings.chatname,document.getElementById("chatopponent").value=localsettings.chatopponent,document.getElementById("instruct_starttag").value=localsettings.instruct_starttag,document.getElementById("instruct_endtag").value=localsettings.instruct_endtag,document.getElementById("top_k").value=localsettings.top_k,document.getElementById("top_a").value=localsettings.top_a,document.getElementById("typ_s").value=localsettings.typ_s,document.getElementById("tfs_s").value=localsettings.tfs_s,document.getElementById("generate_images").value=localsettings.generate_images,document.getElementById("enhanced_chat_ui").checked=localsettings.enhanced_chat_ui,document.getElementById("enhanced_instruct_ui").checked=localsettings.enhanced_instruct_ui,document.getElementById("multiline_replies").checked=localsettings.multiline_replies,document.getElementById("allow_continue_chat").checked=localsettings.allow_continue_chat,document.getElementById("idle_responses").value=localsettings.idle_responses,document.getElementById("idle_duration").value=localsettings.idle_duration,document.getElementById("adventure_context_mod").checked=localsettings.adventure_context_mod,document.getElementById("instruct_has_markdown").checked=localsettings.instruct_has_markdown,document.getElementById("auto_ctxlen").checked=localsettings.auto_ctxlen,document.getElementById("auto_genamt").checked=localsettings.auto_genamt,pendingstyle=localsettings.image_styles;let e=localsettings.sampler_order.toString();document.getElementById("sampler_order").value=e;let t="";for(var n=0;n<presets.length;++n)t+='<option value="'+n+'" title="'+presets[n].description+'">'+presets[n].preset+"</option>";t+='<option value="9999" title="User Defined Settings">[Custom]</option>',document.getElementById("presets").innerHTML=t,document.getElementById("presets").value=localsettings.last_selected_preset;var o='<option value="0">Disabled</option>';if("speechSynthesis"in window){let e=window.speechSynthesis.getVoices();console.log("speech synth available: "+e.length);for(n=0;n<e.length;++n)o+='<option value="'+(n+1)+'">'+e[n].name+"</option>"}else console.log("No speech synth available");document.getElementById("ttsselect").innerHTML=o,document.getElementById("ttsselect").value=localsettings.speech_synth,document.getElementById("beep_on").checked=localsettings.beep_on,toggle_opmode();let r="";for(n=0;n<stablemodels.length;++n)r+='<option value="'+stablemodels[n].name+" ("+stablemodels[n].count+')">';document.getElementById("sdmodels").innerHTML=r,document.getElementById("img_autogen").checked=localsettings.img_autogen,document.getElementById("save_images").checked=localsettings.save_images,document.getElementById("img_allownsfw").checked=localsettings.img_allownsfw}function toggle_preset(){let e=document.getElementById("presets").value,t=presets[e];t&&(temp_changingpreset=!0,document.getElementById("temperature").value=document.getElementById("temperature_slide").value=t.temp,document.getElementById("max_length").value=document.getElementById("max_length_slide").value=t.genamt,document.getElementById("top_k").value=t.top_k,document.getElementById("top_p").value=document.getElementById("top_p_slide").value=t.top_p,document.getElementById("top_a").value=t.top_a,document.getElementById("typ_s").value=t.typical,document.getElementById("tfs_s").value=t.tfs,document.getElementById("rep_pen").value=document.getElementById("rep_pen_slide").value=t.rep_pen,document.getElementById("rep_pen_range").value=t.rep_pen_range,document.getElementById("rep_pen_slope").value=t.rep_pen_slope,document.getElementById("sampler_order").value=t.sampler_order.toString())}function validate_sd_model(){var e=document.getElementById("generate_images").value;let t=!1;for(var n=0;n<stablemodels.length;++n){if(e==stablemodels[n].name+" ("+stablemodels[n].count+")"||e==stablemodels[n].name){document.getElementById("generate_images").value=stablemodels[n].name,t=!0;break}}t||"*"==e||(document.getElementById("generate_images").value="")}function clear_sd_model(){document.getElementById("generate_images").value="",image_models_fetched||fetch_image_models(display_settings)}function validate_samplers(e=!1){let t=document.getElementById("sampler_order").value.split(","),n=[0,1,2,3,4,5,6],o=!0;for(a in t){let e=parseInt(t[a],10);!isNaN(e)&&n.includes(e)?(t[a]=e,n[e]=void 0):o=!1}7==t.length&&o?(e&&(localsettings.sampler_order=t),document.getElementById("sampler_order").value=t.toString()):(e&&(localsettings.sampler_order=defaultsettings.sampler_order),document.getElementById("sampler_order").value=defaultsettings.sampler_order.toString())}var temp_changingpreset=!1;function setting_tweaked(){temp_changingpreset?temp_changingpreset=!1:document.getElementById("presets").value=9999}function toggle_invert_colors(){localsettings.invert_colors?document.body.classList.add("invert_colors"):document.body.classList.remove("invert_colors")}function confirm_settings(){localsettings.max_context_length=document.getElementById("max_context_length").value,localsettings.max_length=document.getElementById("max_length").value,localsettings.temperature=document.getElementById("temperature").value,localsettings.rep_pen=document.getElementById("rep_pen").value,localsettings.rep_pen_slope=document.getElementById("rep_pen_slope").value,localsettings.rep_pen_range=document.getElementById("rep_pen_range").value,localsettings.top_p=document.getElementById("top_p").value,localsettings.autoscroll=!!document.getElementById("autoscroll").checked,localsettings.export_settings=!!document.getElementById("export_settings").checked,localsettings.invert_colors=!!document.getElementById("invert_colors").checked,localsettings.trimsentences=!!document.getElementById("trimsentences").checked,localsettings.trimwhitespace=!!document.getElementById("trimwhitespace").checked,localsettings.persist_session=!!document.getElementById("persist_session").checked,localsettings.enhanced_chat_ui=!!document.getElementById("enhanced_chat_ui").checked,localsettings.enhanced_instruct_ui=!!document.getElementById("enhanced_instruct_ui").checked,localsettings.multiline_replies=!!document.getElementById("multiline_replies").checked,localsettings.allow_continue_chat=!!document.getElementById("allow_continue_chat").checked,localsettings.idle_responses=document.getElementById("idle_responses").value,localsettings.idle_duration=document.getElementById("idle_duration").value,localsettings.adventure_context_mod=!!document.getElementById("adventure_context_mod").checked,localsettings.instruct_has_markdown=!!document.getElementById("instruct_has_markdown").checked,localsettings.generate_images=document.getElementById("generate_images").value,localsettings.opmode=document.getElementById("opmode").value,localsettings.chatname=document.getElementById("chatname").value,null!=localsettings.chatname&&""!=localsettings.chatname||(localsettings.chatname="You"),localsettings.chatopponent=document.getElementById("chatopponent").value.trim(),localsettings.instruct_starttag=document.getElementById("instruct_starttag").value,null!=localsettings.instruct_starttag&&""!=localsettings.instruct_starttag||(localsettings.instruct_starttag="\\n### Instruction:\\n"),localsettings.instruct_endtag=document.getElementById("instruct_endtag").value,null!=localsettings.instruct_endtag&&""!=localsettings.instruct_endtag||(localsettings.instruct_endtag="\\n### Response:\\n"),localsettings.top_k=document.getElementById("top_k").value,localsettings.top_a=document.getElementById("top_a").value,localsettings.typ_s=document.getElementById("typ_s").value,localsettings.tfs_s=document.getElementById("tfs_s").value,localsettings.speech_synth=document.getElementById("ttsselect").value,localsettings.beep_on=!!document.getElementById("beep_on").checked,localsettings.auto_ctxlen=!!document.getElementById("auto_ctxlen").checked,localsettings.auto_genamt=!!document.getElementById("auto_genamt").checked,localsettings.image_styles=pendingstyle,localsettings.img_autogen=!!document.getElementById("img_autogen").checked,localsettings.save_images=!!document.getElementById("save_images").checked,localsettings.img_allownsfw=!!document.getElementById("img_allownsfw").checked,localsettings.generate_images?(document.getElementById("btn_genimg").classList.remove("hidden"),document.getElementById("btn_genimg2").classList.remove("hidden")):(document.getElementById("btn_genimg").classList.add("hidden"),document.getElementById("btn_genimg2").classList.add("hidden")),localsettings.enhanced_chat_ui&&3==localsettings.opmode&&document.getElementById("allowediting")&&(document.getElementById("allowediting").checked=!1,toggle_editable()),validate_samplers(!0),localsettings.last_selected_preset=document.getElementById("presets").value,localsettings.max_context_length=cleannum(localsettings.max_context_length,8,99999),localsettings.max_length=cleannum(localsettings.max_length,1,localsettings.max_context_length-1),localsettings.temperature=cleannum(localsettings.temperature,.01,5),localsettings.rep_pen=cleannum(localsettings.rep_pen,.1,5),localsettings.rep_pen_range=cleannum(localsettings.rep_pen_range,0,8192),localsettings.rep_pen_slope=cleannum(localsettings.rep_pen_slope,0,20),localsettings.top_p=cleannum(localsettings.top_p,.002,1),localsettings.top_k=cleannum(Math.floor(localsettings.top_k),0,300),localsettings.top_a=cleannum(localsettings.top_a,0,1),localsettings.typ_s=cleannum(localsettings.typ_s,0,1),localsettings.tfs_s=cleannum(localsettings.tfs_s,0,1),toggle_invert_colors(),autosave(),hide_popups(),render_gametext()}function toggle_opmode(){document.getElementById("chatnamesection").classList.add("hidden"),document.getElementById("adventuresection").classList.add("hidden"),document.getElementById("instructsection").classList.add("hidden"),document.getElementById("idlesection").classList.add("hidden"),1==document.getElementById("opmode").value&&document.getElementById("idlesection").classList.remove("hidden"),3==document.getElementById("opmode").value&&(document.getElementById("chatnamesection").classList.remove("hidden"),document.getElementById("idlesection").classList.remove("hidden")),2==document.getElementById("opmode").value&&document.getElementById("adventuresection").classList.remove("hidden"),4==document.getElementById("opmode").value&&document.getElementById("instructsection").classList.remove("hidden")}function prompt_overwrite(){msgboxYesNo("You already have an existing persistent story. Do you want to overwrite it?","Overwrite Warning",confirm_overwrite,hide_popups)}function confirm_overwrite(){pending_found_story&&""!=pending_found_story&&(import_share_story(pending_found_story),pending_found_story=null),hide_popups()}function display_newgame(){document.getElementById("newgamecontainer").classList.remove("hidden")}function confirm_newgame(){localmode||document.getElementById("keep_ai_selected").checked||(selected_models=[],selected_workers=[],localsettings.opmode=1),restart_new_game(),hide_popups()}function confirm_memory(){current_memory=document.getElementById("memorytext").value,current_anote=document.getElementById("anotetext").value,current_anotetemplate=document.getElementById("anotetemplate").value,anote_strength=document.getElementById("anote_strength").value,extrastopseq=document.getElementById("extrastopseq").value,hide_popups(),render_gametext()}let temp_automem_store="";function autogenerate_summary_memory(){temp_automem_store=document.getElementById("memorytext").value;let e=()=>{pending_response_id="-1",waiting_for_autosummary=!0;let e=Math.floor(3.35*localsettings.max_context_length)-100,t=concat_gametext(!0,"");t=end_trim_to_sentence(t,!0),t.substring(t.length-e);let n=t.length>1800;t+="\n### Instruction:Summarize the above text in a single paragraph of up to "+(n?"ten":"five")+" detailed sentences.\n### Response:";let o={prompt:t,params:{n:1,max_context_length:localsettings.max_context_length,max_length:n?200:150,rep_pen:localsettings.rep_pen,temperature:localsettings.temperature,top_p:localsettings.top_p,top_k:localsettings.top_k,top_a:localsettings.top_a,typical:localsettings.typ_s,tfs:localsettings.tfs_s,rep_pen_range:localsettings.rep_pen_range,rep_pen_slope:localsettings.rep_pen_slope,sampler_order:localsettings.sampler_order},models:selected_models.map((e=>e.name))};o.workers=selected_workers.map((e=>e.id)),dispatch_submit_generation(o),render_gametext(),document.getElementById("memorytext").value="[<|Generating summary, do not close window...|>]"};0==gametext_arr.length||1==gametext_arr.length&&""==gametext_arr[0].trim()?console.log("Cannot summarize nothing."):""!=temp_automem_store.trim()?msgboxYesNo("This will modify existing memory. Proceed?","Confirm Modify",(()=>{document.getElementById("yesnocontainer").classList.add("hidden"),e()}),(()=>{document.getElementById("yesnocontainer").classList.add("hidden")})):e()}function handle_incoming_autosummary(e){waiting_for_autosummary=!1;let t=(e=replaceAll(e=(e=e.trim()).split("###")[0],"\n\n","\n")).split("\n"),n=200;if((e=t[0]).length<100&&t.length>1)for(var o=1;o<t.length&&(n-=t[o].length,t[o].trim().length>5&&(e+="\n"+t[o]),!(n<=0));++o);e=end_trim_to_sentence(e,!0),""==temp_automem_store.trim()?document.getElementById("memorytext").value="[Summary: "+e+"]":document.getElementById("memorytext").value=temp_automem_store+"\n\n[Summary Continued: "+e+"]"}function clear_poll_flags(){pending_response_id="",poll_in_progress=!1,synchro_polled_response=null,synchro_pending_stream="",waiting_for_autosummary=!1}function restart_new_game(){idle_timer=0,gametext_arr=[],redo_arr=[],last_request_str="No Requests Available",retry_prev_text="",redo_prev_text="",nextgeneratedimagemilestone=generateimagesinterval,pending_response_id="",synchro_polled_response=null,synchro_pending_stream="",waiting_for_autosummary=!1,current_memory="",current_anote="",current_wi=[],pending_context_preinjection="",current_anotetemplate="[Author's note: <|>]",loaded_storyobj=generate_base_storyobj(),document.getElementById("input_text").value="",document.getElementById("cht_inp").value="",image_db={},completed_imgs_meta={},localsettings.adventure_is_action=!1,prev_hl_chunk=null,last_token_budget="",last_known_filename="",render_gametext()}function btn_editmode(){gametext_arr.length>0&&(document.getElementById("allowediting").checked=!0,toggle_editable())}function toggle_editable(){0==gametext_arr.length?selected_models.length>0||selected_workers.length>0?document.getElementById("allowediting").checked&&gametext_arr.push(""):document.getElementById("allowediting").checked=!1:1==gametext_arr.length&&""==gametext_arr[0]&&gametext_arr.pop(),render_gametext()}function end_trim_to_sentence(e,t=!1){let n=-1,o=[".","!","?","`","*",'"',")","}","`","]",";"];for(let t=0;t<o.length;++t)n=Math.max(n,e.lastIndexOf(o[t]));if(t){let t=e.lastIndexOf("\n");n=Math.max(n,t)}return n>0?e.substring(0,n+1).trimEnd():e.trimEnd()}function start_trim_to_sentence(e){let t=e.indexOf("."),n=e.indexOf("!"),o=e.indexOf("?"),r=e.indexOf("\n"),s=t,l=!1;return n>0&&n<s&&(s=n),o>0&&o<s&&(s=o),r>0&&r<s&&(s=r,l=!0),s>0?l?e.substring(s+1):e.substring(s+2):e}function handle_typing(e){var t=(e=e||window.event).keyCode||e.which;if(!e.shiftKey&&13==t){let t=!!document.getElementById("entersubmit").checked,n=""!=document.getElementById("input_text").value;t&&(e.preventDefault(),n&&!document.getElementById("btnsend").disabled&&submit_generation())}}function show_abort_button(e){e?(document.getElementById("abortgen").classList.remove("hidden"),document.getElementById("chat_msg_send_btn_abort").classList.remove("hidden")):(document.getElementById("abortgen").classList.add("hidden"),document.getElementById("chat_msg_send_btn_abort").classList.add("hidden"))}function abort_generation(){let e=pending_response_id;if(is_using_custom_ep()&&""!=pending_response_id&&""!=synchro_pending_stream&&(synchro_polled_response=synchro_pending_stream,poll_in_progress=!1,poll_pending_response()),console.log("Generation "+pending_response_id+" aborted"),clear_poll_flags(),render_gametext(),pending_response_horde&&e&&""!=e&&!is_using_custom_ep()){let t=pending_response_horde.output_endpoint+"/"+e;fetch(t,{method:"DELETE"}).then((e=>e.json())).then((e=>{console.log(e)})).catch((e=>{console.error("Error:",e)}))}else is_using_newer_kcpp()&&fetch(custom_kobold_endpoint+koboldcpp_abort_endpoint,{method:"POST",headers:{"Content-Type":"application/json"}}).then((e=>e.json())).then((e=>{})).catch((e=>{console.error("Error:",e)}));show_abort_button(!1)}function manual_gen_image(){let e=concat_gametext(!0,"");var t=e.length;if(t>0){var n=e.substring(t-300,t);(n=end_trim_to_sentence(n=start_trim_to_sentence(n),!0)).length>0&&(generate_new_image(n),nextgeneratedimagemilestone=t+generateimagesinterval,document.getElementById("btn_genimg").disabled=!0,document.getElementById("btn_genimg2").disabled=!0,setTimeout((()=>{document.getElementById("btn_genimg").disabled=!1,document.getElementById("btn_genimg2").disabled=!1}),1e4))}}function submit_generation(){let e=document.getElementById("input_text").value,t=!1;if(""!=e.trim()||gametext_arr.length>0||""!=current_memory||""!=current_anote){if(waiting_for_autosummary=!1,idle_timer=0,idle_triggered_counter=0,localsettings.speech_synth>0&&"speechSynthesis"in window){let t=new window.SpeechSynthesisUtterance(e);t.voice=window.speechSynthesis.getVoices()[localsettings.speech_synth-1],window.speechSynthesis.speak(t)}4==localsettings.opmode&&""!=e&&(e=get_instruct_starttag(!1)+e+get_instruct_endtag(!1)),3==localsettings.opmode&&""!=e?e="\n"+localsettings.chatname+": "+e:3==localsettings.opmode&&""==e.trim()&&(e=""),2==localsettings.opmode&&""!=e&&localsettings.adventure_is_action&&(e="\n\n> "+e+"\n\n"),2==localsettings.opmode&&""!=e&&0==gametext_arr.length&&(localsettings.adventure_is_action||(localsettings.adventure_is_action=!0,""==current_memory.trim()&&(t=!0))),""!=e&&gametext_arr.push(e),redo_arr=[],retry_prev_text="",redo_prev_text="",document.getElementById("input_text").value="",pending_response_id="-1";let l=document.getElementById("maintxtloader");if(l){l.classList.remove("greenloader"),l.classList.remove("redloader");let e=document.getElementById("outerloadernum");e&&(e.innerText="")}let a=localsettings.max_context_length,i=localsettings.max_length;if(!is_using_custom_ep()&&(localsettings.auto_genamt||localsettings.auto_ctxlen)){let e=selected_workers;if((null==e||0==e.length)&&selected_models&&selected_models.length>0){e=[];for(let t=0;t<worker_data.length;++t){let n=worker_data[t];for(let t=0;t<selected_models.length;++t){let o=selected_models[t];if(o.cluster==n.cluster&&n.models.includes(o.name)){e.push(n);break}}}}for(let t=0;t<e.length;++t){let n=e[t];localsettings.auto_ctxlen&&(a=Math.min(n.max_context_length,a)),localsettings.auto_genamt&&(i=Math.min(n.max_length,i))}}let c=Math.floor(3.35*a);null!=current_memory&&""!=current_memory.trim()||(c=Math.floor(4.6*a));let d=concat_gametext(!0,"");if(d=d.replace(/\xA0/g," "),localsettings.trimwhitespace&&(d=d.replace(/[\t ]+$/,""),d=d.replace(/[\r\n]+/g,"\n")),2==localsettings.opmode&&localsettings.adventure_context_mod){let e="[Interactive Fiction: Game Mode Enabled]\n[You are playing a choose-your-own-adventure game. Please input action.]\n";e+="\n\n> Look\n\nYou look around, observing yourself and your surroundings.\n\n",d=e+d}if(3==localsettings.opmode){let t=localsettings.chatopponent,r=!1;if(t.includes("||$||")){let e=t.split("||$||");e=e.filter((e=>e&&""!=e)),e=e.map((e=>e.trim())),t=e[Math.floor(Math.random()*e.length)],r=e.length>1}let s=localsettings.chatname;null!=t&&""!=t||(t="");var n=new RegExp("\n(?!"+localsettings.chatname+").+?: ","gi"),o=(current_memory+current_anote+d).match(n);if(""==t&&null!=o&&o.length>0){let e=o[0].replace(": ","");e=e.trim(),""!=e&&(t=e)}if(0==current_anote.length&&0==current_memory.length&&gametext_arr.length>0&&gametext_arr[0].startsWith("\n"+s+": ")){let e="[The following is an interesting chat message log between "+s+" and "+t+".]\n\n"+localsettings.chatname+": Hi.\n"+t+": Hello.";""==t&&(e="[The following is an interesting chat message log between "+s+" and someone else.]\n\n"+localsettings.chatname+": Hi."),r&&(e="[The following is an interesting chat message log between "+s+" and multiple others.]\n\n"+localsettings.chatname+": Hi."),d=e+d}""!=t?(t=replaceAll(t,"\n",""),pending_context_preinjection="\n"+t+":"):pending_context_preinjection="\n",localsettings.allow_continue_chat&&""==e.trim()?pending_context_preinjection="":d+=pending_context_preinjection}if(""!=localsettings.generate_images&&3!=localsettings.opmode&&4!=localsettings.opmode&&localsettings.img_autogen)if(2==localsettings.opmode){if(e.startsWith("\n\n> "))generate_new_image(start_trim_to_sentence(d.substring(r-200,r)))}else{var r=d.length;if(r>nextgeneratedimagemilestone)nextgeneratedimagemilestone=r+generateimagesinterval,generate_new_image(end_trim_to_sentence(start_trim_to_sentence(d.substring(r-300,r)),!0))}let m=Math.floor(.9*c),u=current_memory.substring(current_memory.length-m);if(null!=u&&""!=u){1!=urlParams.get("nomemorynewline")&&(u+="\n")}let _=d;if(localsettings.case_sensitive_wi||(_=_.toLowerCase()),current_wi.length>0)for(var s=0;s<current_wi.length;++s){let e=current_wi[s];if(null==e.key||""==e.key)continue;let t=e.selective&&(""==e.keysecondary||null==e.keysecondary),n=e.key.split(","),o=!1;if(e.constant)o=!0;else if(!e.selective||t)o=localsettings.case_sensitive_wi?n.some((e=>_.includes(e.trim()))):n.some((e=>_.includes(e.trim().toLowerCase())));else{let t=e.keysecondary.split(",");if(localsettings.case_sensitive_wi){let e=n.some((e=>_.includes(e.trim()))),r=t.some((e=>_.includes(e.trim())));o=e&&r}else{let e=n.some((e=>_.includes(e.trim().toLowerCase()))),r=t.some((e=>_.includes(e.trim().toLowerCase())));o=e&&r}}o&&(u+=e.content+"\n")}let g=current_anotetemplate.replace("<|>",current_anote);if(g=g.substring(g.length-m),0==current_anote.length&&(g=""),u.length>0||current_anote.length>0){d=d.substring(d.length-c);let e=u.length+d.length+g.length-c;d=d.substring(e);let t=anote_strength,n=d.length-t;for(let e=0;e<10&&(n>=0&&n<d.length&&" "!=d[n]&&"."!=d[n]&&"!"!=d[n]&&"?"!=d[n]&&"\n"!=d[n]);++e)++n;n=clamp(n,0,d.length),d=d.slice(0,n)+g+d.slice(n),d=u+d}last_token_budget=d.length+"/"+c;let p={prompt:d,params:{n:1,max_context_length:a,max_length:i,rep_pen:localsettings.rep_pen,temperature:localsettings.temperature,top_p:localsettings.top_p,top_k:localsettings.top_k,top_a:localsettings.top_a,typical:localsettings.typ_s,tfs:localsettings.tfs_s,rep_pen_range:localsettings.rep_pen_range,rep_pen_slope:localsettings.rep_pen_slope,sampler_order:localsettings.sampler_order},models:selected_models.map((e=>e.name))};p.workers=selected_workers.map((e=>e.id)),t?pending_response_id="":dispatch_submit_generation(p),render_gametext()}}function dispatch_submit_generation(e){if(console.log(e),last_request_str=JSON.stringify(e),startTimeTaken(),is_using_custom_ep())if(console.log("submit custom api"),pending_response_id="submit-v1-dummy-id",poll_ticks_passed=0,poll_in_progress=!1,synchro_polled_response=null,synchro_pending_stream="",""!=custom_kobold_endpoint){let t=e.prompt;(e=e.params).prompt=t;let n=!!document.getElementById("remoteconsolelog").checked;if(e.quiet=!n,kobold_endpoint_version&&""!=kobold_endpoint_version&&compare_version_str(kobold_endpoint_version,"1.2.1")>0){if(2==localsettings.opmode&&(e.stop_sequence=["\n> "]),3==localsettings.opmode&&(e.stop_sequence=[localsettings.chatname+":"],"You"!=localsettings.chatname&&(e.stop_sequence=[localsettings.chatname+":","\n"+localsettings.chatname+" "]),localsettings.chatopponent.includes("||$||"))){let t=localsettings.chatopponent.split("||$||");t=t.filter((e=>e&&""!=e)),t=t.map((e=>e.trim()));for(let n=0;n<t.length;++n)e.stop_sequence.push(t[n]+":")}if(4==localsettings.opmode){let t=get_instruct_starttag(!0),n=get_instruct_endtag(!0);e.stop_sequence=[t,n]}if(""!=extrastopseq){let t=replaceAll(extrastopseq,"\\n","\n").split("||$||");t.length>0&&!e.stop_sequence&&(e.stop_sequence=[]);for(let n=0;n<t.length;++n)t[n]&&""!=t[n]&&e.stop_sequence.push(t[n])}}let o=should_use_pseudostreaming(),r=urlParams.get("streamamount"),s=8;null!=r&&r>0&&(s=r),kobold_api_stream(apply_proxy_url(custom_kobold_endpoint+kobold_custom_gen_endpoint),e,e.max_length,"",o?s:4096)}else if(""!=custom_oai_key){let t=custom_oai_endpoint+oai_submit_endpoint,n=e.params.rep_pen-1,o={max_tokens:e.params.max_length,model:custom_oai_model,presence_penalty:n,temperature:e.params.temperature,top_p:e.params.top_p,logit_bias:{50256:-100}};"gpt-3.5-turbo"==custom_oai_model||"gpt-3.5-turbo-16k"==custom_oai_model||"gpt-4"==custom_oai_model||"gpt-4-32k"==custom_oai_model?(t=custom_oai_endpoint+oai_submit_endpoint_turbo,document.getElementById("jailbreakprompt")&&document.getElementById("jailbreakprompt").checked&&""!=document.getElementById("jailbreakprompttext").value?o.messages=[{role:"system",content:document.getElementById("jailbreakprompttext").value},{role:"user",content:e.prompt}]:o.messages=[{role:"user",content:e.prompt}]):o.prompt=e.prompt,fetch(t,{method:"POST",headers:{"Content-Type":"application/json","x-api-key":custom_oai_key,Authorization:"Bearer "+custom_oai_key},body:JSON.stringify(o),referrerPolicy:"no-referrer"}).then((e=>e.json())).then((e=>{if(console.log("sync finished response: "+JSON.stringify(e)),""!=custom_oai_key&&null!=e.choices&&e.choices.length>0){let t=e.choices[0];t.text?synchro_polled_response=t.text:t.message?synchro_polled_response=t.message.content:(console.error("Error, unknown OAI response"),clear_poll_flags(),render_gametext(),msgbox("Error, unknown OAI response"))}else console.error("error occurred in OAI generation"),clear_poll_flags(),render_gametext(),msgbox("Error occurred during text generation: "+formatError(e))})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),msgbox("Error while submitting prompt: "+e)}))}else if(""!=custom_scale_key){let t=cors_proxy+"?"+scale_submit_endpoint+custom_scale_ID,n={input:{input:e.prompt}};fetch(t,{method:"POST",headers:{"Content-Type":"application/json",Authorization:"Basic "+custom_scale_key},body:JSON.stringify(n),referrerPolicy:"no-referrer"}).then((e=>e.json())).then((e=>{console.log("sync finished response: "+JSON.stringify(e)),""!=custom_scale_key&&null!=e.output&&""!=e.output?synchro_polled_response=e.output:(console.error("error occurred in Scale generation"),clear_poll_flags(),render_gametext(),msgbox("Error occurred during text generation: "+formatError(e)))})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),msgbox("Error while submitting prompt: "+e)}))}else if(""!=custom_claude_key){let t=cors_proxy+"?"+(custom_claude_endpoint+claude_submit_endpoint),n={prompt:e.prompt,max_tokens_to_sample:e.params.max_length,model:custom_claude_model,top_k:e.params.top_k<=0?-1:e.params.top_k,temperature:e.params.temperature,top_p:e.params.top_p};document.getElementById("clauderenamecompat").checked&&(n.prompt.toLowerCase().trim().startsWith("human:")||(n.prompt="Human: "+n.prompt),n.prompt.toLowerCase().trim().endsWith("assistant:")||(1==localsettings.opmode?n.prompt=n.prompt+" \nAssistant: Here is a continuation of the story: \nAssistant:":n.prompt=n.prompt+" (cont.) Assistant:")),fetch(t,{method:"POST",headers:{"Content-Type":"application/json","x-api-key":custom_claude_key,Authorization:"Bearer "+custom_claude_key},body:JSON.stringify(n),referrerPolicy:"no-referrer"}).then((e=>e.json())).then((e=>{console.log("sync finished response: "+JSON.stringify(e)),""!=custom_claude_key&&null!=e.completion&&""!=e.completion?synchro_polled_response=e.completion:(console.error("error occurred in Claude generation"),clear_poll_flags(),render_gametext(),msgbox("Error occurred during text generation: "+formatError(e)))})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),msgbox("Error while submitting prompt: "+e)}))}else console.log("Unknown sync endpoint!");else{console.log("submit v2 api");let t=find_text_horde(localsettings.home_cluster);if(selected_workers.length>0){const e=selected_workers.filter((e=>e.cluster==localsettings.home_cluster)),n=selected_workers.filter((e=>e.cluster!=localsettings.home_cluster));if(0==e.length&&n.length>0){let e=find_text_horde(n[0].cluster);e&&(t=e)}}else if(selected_models.length>0){const e=selected_models.filter((e=>e.cluster==localsettings.home_cluster)),n=selected_models.filter((e=>e.cluster!=localsettings.home_cluster));if(0==e.length&&n.length>0){let e=find_text_horde(n[0].cluster);e&&(t=e)}}let n=t.baseurl==localsettings.home_cluster?localsettings.my_api_key:defaultsettings.my_api_key,o=t.client_agent,r={"Content-Type":"application/json",apikey:n};null!=o&&(r["Client-Agent"]=o),fetch(t.submit_endpoint,{method:"POST",headers:r,body:JSON.stringify(e)}).then((e=>e.json())).then((e=>{console.log("Success:",e),e.id&&""!=e.id?(pending_response_id=e.id,pending_response_horde=t,poll_ticks_passed=0,console.log("awaiting response for "+pending_response_id)):(clear_poll_flags(),render_gametext(),""!=e.message?msgbox(e.message):msgbox("Unspecified error while submitting prompt"))})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),msgbox("Error while submitting prompt: "+e)}))}}function sanitize_horde_image_prompt(e){if(null==e||""==e)return"";return(e=(e=(e=(e=(e=e.replace(/\b(girl)\b/gim,"woman")).replace(/\b(boy)\b/gim,"man")).replace(/\b(girls)\b/gim,"women")).replace(/\b(boys)\b/gim,"men")).replace(/\b(under.age|under.aged|underage|underaged|loli|pedo|pedophile|(\w+).year.old|(\w+).years.old|minor|prepubescent|minors|shota)\b/gim,"")).match(/\b(cock|ahegao|hentai|uncensored|lewd|cocks|deepthroat|deepthroating|dick|dicks|cumshot|lesbian|fuck|fucked|fucking|sperm|naked|nipples|tits|boobs|breasts|boob|breast|topless|ass|butt|fingering|masturbate|masturbating|bitch|blowjob|pussy|piss|asshole|dildo|dildos|vibrator|erection|foreskin|handjob|nude|penis|porn|vibrator|virgin|vagina|vulva|threesome|orgy|bdsm|hickey|condom|testicles|anal|bareback|bukkake|creampie|stripper|strap-on|missionary|clitoris|clit|clitty|cowgirl|fleshlight|sex|buttplug|milf|oral|sucking|bondage|orgasm|scissoring|railed|slut|sluts|slutty|cumming|cunt|faggot|sissy|anal|anus|cum|semen|scat|nsfw|xxx|explicit|erotic|horny|aroused|jizz|moan|rape|raped|raping|throbbing|humping)\b/gim)&&(e=(e=e.replace(/\b(youngster|infant|baby|toddler|child|teen|kid|kiddie|kiddo|teenager|student|preteen|pre.teen)\b/gim,"person")).replace(/\b(young|younger|youthful|youth|small|smaller|smallest|girly|boyish|lil|tiny|teenaged|lit[tl]le|school.aged|school|highschool|kindergarten|teens|children|kids)\b/gim,"")),e}function generate_new_image(e){localsettings.image_styles&&""!=localsettings.image_styles&&(e=localsettings.image_styles+" "+e),filter_enabled&&(e=sanitize_horde_image_prompt(e)),console.log("Generating image for: "+e);let t=[];t="*"==localsettings.generate_images?[]:[localsettings.generate_images];let n={prompt:e+" ### disfigured, ugly, deformed, poorly, censor, censored, blurry, lowres, fused, malformed, watermark, misshapen, duplicated, grainy, distorted, signature",params:{cfg_scale:7,sampler_name:"k_euler_a",height:512,width:512,steps:20,karras:!1,n:1,seed:"",post_processing:[]},models:t,nsfw:!!localsettings.img_allownsfw,censor_nsfw:!localsettings.img_allownsfw,trusted_workers:!1,replacement_filter:!0,r2:!1};fetch(stablehorde_submit_endpoint,{method:"POST",headers:{"Content-Type":"application/json","Client-Agent":default_client_agent,apikey:localsettings.my_api_key},body:JSON.stringify(n)}).then((e=>e.json())).then((t=>{if(console.log("genimg result:",t),t.id&&""!=t.id){let n="[<|p|"+t.id+"|p|>]";gametext_arr.push(n),image_db[t.id]={done:!1,queue:"Starting",result:"",alt:e},console.log("New image queued "+n)}else msgbox("Image generation failed: "+t.message)})).catch((e=>{console.error("Error:",e),msgbox("Image generation error: "+e)}))}function click_image(e){if(e){document.getElementById("zoomedimgcontainer").classList.remove("hidden"),document.getElementById("zoomedimg").src=e.src;let t=e.title;t&&""!=t?(t=replaceAll(t,"<br>"," "),document.getElementById("zoomedimgdesc").innerText=t):document.getElementById("zoomedimgdesc").innerText="No Saved Description"}}function delete_curr_image(){let e=document.getElementById("zoomedimg").src;if(e&&""!=e){var t="[<|d|"+e+"|d|>]";for(let e=0;e<gametext_arr.length;++e)if(gametext_arr[e].includes(t)){gametext_arr[e]=gametext_arr[e].replace(t,""),""==gametext_arr[e]&&gametext_arr.splice(e,1);break}render_gametext()}}function render_image_html(e,t="",n=!0){var o=2==localsettings.opmode?160:200;let r=n?"storyimgfloat":"storyimg",s="";if(e&&""!=e){let t=cyrb_hash(e);return null!=completed_imgs_meta[t]&&(s=completed_imgs_meta[t].alt?escapeHtml(completed_imgs_meta[t].alt):""),'<div class="'+r+'"><img src="'+e+'" width='+o+" height="+o+' title="'+s+'" style="border-radius: 6%; cursor: pointer;" onclick="return click_image(this);"></div>'}{let e="Unavailable";if(null!=image_db[t]){let n=image_db[t].queue;s=image_db[t].alt?escapeHtml(image_db[t].alt):"",e=0==n?"Generating":"Starting"==n?n:"Queue: "+n}else console.log("Cannot render "+t);return'<div class="'+r+'" contenteditable="false"><img 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width='+o+" height="+o+' style="border-radius: 6%;" title="'+s+'" alt="'+t+'"><div class="loader2"></div><div class="imagelabel">'+e+"</div></div>"}}function handle_incoming_text(e,t,n,o){if(""!=extrastopseq){let t=replaceAll(extrastopseq,"\\n","\n").split("||$||");if(t.length>0)for(let n=0;n<t.length;++n)if(t[n]&&""!=t[n]){let o=e.indexOf(t[n]);-1!=o&&(e=e.substr(0,o)+t[n])}}if((2==localsettings.opmode||3==localsettings.opmode&&!localsettings.allow_continue_chat||1==localsettings.trimsentences)&&(e=end_trim_to_sentence(e,!0)),2==localsettings.opmode){let t=[];-1!=e.indexOf("\n> ")&&(t=e.split("\n> "),e=t[0])}if(3==localsettings.opmode){let t=e.indexOf(localsettings.chatname+":"),n=e.indexOf("\n"+localsettings.chatname+" "),o=[];if(-1!=t)o=e.split(localsettings.chatname+":");else if(-1!=n&&"You"!=localsettings.chatname)o=e.split("\n"+localsettings.chatname+" ");else if(localsettings.multiline_replies)o.push(e);else if(0==e.indexOf('"')&&e.indexOf('"',1)>0){let t=e.indexOf('"',1);o.push(e.substring(0,t+1))}else o=e.split("\n");let r=o[0];r.length>0&&"\n"==r[r.length-1]&&(r=r.substring(0,r.length-1)),e=r}if(4==localsettings.opmode){let t=get_instruct_starttag(!0),n=get_instruct_endtag(!0),o=e.indexOf(t),r=[];-1!=o&&(r=e.split(t),e=r[0]),o=e.indexOf(n),r=[],-1!=o&&(r=e.split(n),e=r[0])}if(""!=pending_context_preinjection&&(""!=e&&" "!=e[0]&&3==localsettings.opmode&&(e=" "+e),e=pending_context_preinjection+e,pending_context_preinjection=""),localsettings.speech_synth>0&&"speechSynthesis"in window){let t=new window.SpeechSynthesisUtterance(e);t.voice=window.speechSynthesis.getVoices()[localsettings.speech_synth-1],window.speechSynthesis.speak(t)}""!=e&&gametext_arr.push(e),localsettings.beep_on&&playbeep();let r='<a href="#" onclick="show_last_req()">Last request</a> served by <a href="#" onclick="get_and_show_workers()">'+t+'</a> using <span class="color_darkgreen">'+n+"</span> for "+o+" kudos in "+getTimeTaken()+" seconds.";document.getElementById("lastreq").innerHTML=r,document.getElementById("lastreq2").innerHTML=r}function poll_image_db(){console.log("polling for pending images "+Object.keys(image_db).length);for(let e in image_db){let t=image_db[e];0==t.done&&fetch(stablehorde_poll_endpoint+"/"+e).then((e=>e.json())).then((n=>{console.log("pollimg result:",n),1==n.faulted||0==n.is_possible?(msgbox("Pending image generation could not complete."),console.log("removing from images: "+e),delete image_db[e]):1==n.done?(t.done=!0,fetch(stablehorde_output_endpoint+"/"+e).then((e=>e.json())).then((n=>{if(console.log("finalimg recv for "+e),1==n.faulted||0==n.is_possible)msgbox("Pending image generation could not complete."),console.log("removing from images: "+e),delete image_db[e];else{t.queue=0,compressImage("data:image/jpeg;base64,"+n.generations[0].img,(e=>{t.result=e}))}})).catch((t=>{console.error("Error:",t),msgbox("Image poll error: "+t),delete image_db[e]}))):t.queue=null==n.queue_position?"Error":n.queue_position})).catch((t=>{console.error("Error:",t),msgbox("Image poll error: "+t),delete image_db[e]}))}let e=!1;for(var t=0;t<gametext_arr.length;++t)if(/\[<\|p\|.+?\|p\|>\]/.test(gametext_arr[t]))for(let n in image_db){let o=image_db[n],r="[<|p|"+n+"|p|>]";if(gametext_arr[t].includes(r)&&(e=!0,1==o.done&&""!=o.result)){let e="[<|d|"+o.result+"|d|>]";console.log("Replacing with Image: "+r),gametext_arr[t]=gametext_arr[t].replace(r,e),completed_imgs_meta[cyrb_hash(o.result)]={alt:image_db[n].alt},delete image_db[n]}}e&&document.activeElement!=document.getElementById("gametext")&&render_gametext()}function compressImage(e,t){let n=document.createElement("img");n.onload=function(){var e=document.createElement("canvas"),n=e.getContext("2d");e.width=256,e.height=256,n.drawImage(this,0,0,256,256);var o=e.toDataURL("image/jpeg",.33);t(o)},n.src=e}var idle_timer=0,idle_triggered_counter=0;function poll_background_tasks(){let e=1e3*localsettings.idle_duration,t=""==document.getElementById("input_text").value,n=""==document.getElementById("cht_inp").value;if((1==localsettings.opmode||3==localsettings.opmode)&&localsettings.idle_responses>0&&t&&n&&!document.getElementById("btnsend").disabled&&idle_triggered_counter<localsettings.idle_responses){if((idle_timer+=1e3)>e){idle_timer=0;let e=++idle_triggered_counter;submit_generation(),idle_triggered_counter=e}console.log("Idling: "+idle_timer+", "+idle_triggered_counter)}else idle_timer=0}function poll_pending_response(){if(++poll_ticks_passed,is_using_custom_ep()||poll_ticks_passed%3==0)if(show_abort_button(!1),pending_response_id&&"-1"!=pending_response_id&&""!=pending_response_id)if(poll_ticks_passed>4/(.001*poll_interval_base_text)&&show_abort_button(!0),poll_in_progress)console.log("Polling still in progress for id: "+pending_response_id);else if(is_using_custom_ep())if(poll_in_progress=!0,null==synchro_polled_response){console.log("v1 still awaiting reply");let e=should_use_pseudostreaming();!!!document.getElementById("pseudostreaming").checked||e||waiting_for_autosummary||poll_ticks_passed%2!=0?poll_in_progress=!1:fetch(custom_kobold_endpoint+koboldcpp_check_endpoint,{method:"POST",headers:{"Content-Type":"application/json"}}).then((e=>e.json())).then((e=>{e&&null!=e.results&&e.results.length>0&&e.results[0].text&&pending_response_id&&""!=pending_response_id&&(synchro_pending_stream=e.results[0].text,render_gametext()),poll_in_progress=!1})).catch((e=>{console.error("Error:",e),poll_in_progress=!1}))}else{console.log("v1 handle recv reply"),pending_response_id="",poll_in_progress=!1;let e=synchro_polled_response;if(null!=e&&""!=e){let t=e,n="Custom Endpoint",o="0",r=selected_models.length>0?selected_models[0].name:"Unknown Model";waiting_for_autosummary?handle_incoming_autosummary(t):handle_incoming_text(t,n,r,o)}synchro_polled_response=null,synchro_pending_stream="",show_abort_button(!1),render_gametext()}else console.log("v2 Polling started for pending id: "+pending_response_id),poll_in_progress=!0,fetch(pending_response_horde.polling_endpoint+"/"+pending_response_id).then((e=>e.json())).then((e=>{if(null!=e.message||1==e.faulted||0==e.is_possible){console.log("Gave up on failed attempt"),clear_poll_flags(),render_gametext(),show_abort_button(!1);let t="Error encountered during text generation!\n";null!=e.message&&(t+=e.message),1==e.faulted&&(t+="Fault encountered during text generation."),0==e.is_possible&&(t+="No workers were able to generate text with your request."),msgbox(t)}else if(1==e.done)setTimeout((()=>{console.log("fetching completed generation for "+pending_response_id),fetch(pending_response_horde.output_endpoint+"/"+pending_response_id).then((e=>e.json())).then((e=>{if(console.log("Finished "+pending_response_id+": "+JSON.stringify(e)),pending_response_id="",poll_in_progress=!1,null!=e.generations&&e.generations.length>0){let t=e.generations[0].text,n=e.generations[0].worker_name,o=e.generations[0].model,r=e.kudos;waiting_for_autosummary?handle_incoming_autosummary(t):handle_incoming_text(t,n,o,r)}render_gametext(),show_abort_button(!1)})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),show_abort_button(!1),msgbox("Error encountered during text generation!")}))}),500);else{poll_in_progress=!1;let t=document.getElementById("maintxtloader");if(t){t.classList.remove("greenloader"),t.classList.remove("redloader"),e.queue_position>0?t.classList.add("redloader"):1==e.processing&&0==e.queue_position&&t.classList.add("greenloader");let n=document.getElementById("outerloadernum");n&&(n.innerText=0==e.queue_position?"":e.queue_position)}console.log("Still awaiting "+pending_response_id+": "+JSON.stringify(e))}})).catch((e=>{console.error("Error:",e),clear_poll_flags(),render_gametext(),show_abort_button(!1),msgbox("Error encountered during text generation!")}));else console.log("Nothing to update: "+pending_response_id)}function click_gametext(){if(document.getElementById("allowediting").checked){if(void 0!==window.getSelection){const e=window.getSelection();null!=e.focusNode&&null!=e.focusNode.parentElement&&e.focusNode.parentElement.classList.contains("txtchunk")&&(null!=prev_hl_chunk&&prev_hl_chunk.classList.remove("hlchunk"),(prev_hl_chunk=e.focusNode.parentElement).classList.add("hlchunk")),idle_timer=0}}}function merge_edit_field(){if(gametext_arr.length>0&&document.getElementById("allowediting").checked){let t=concat_gametext(!0,"\n","",""),n=document.getElementById("gametext");if(t!=n.innerText){gametext_arr=[],redo_arr=[],retry_prev_text="",redo_prev_text="",n.querySelectorAll("div.storyimg,div.storyimgfloat").forEach((e=>{let t=e.getElementsByTagName("img")[0];e.replaceWith(null==t.alt||""==t.alt?"[<|d|"+t.src+"|d|>]":"[<|p|"+t.alt+"|p|>]")}));let t=[];n.querySelectorAll("span.txtchunk").forEach((e=>{t.push(e.innerText)})),n.innerHTML=n.innerHTML.replace(/<span class="(.+?)">(.+?)<\/span>/g,"$2"),n.innerHTML=n.innerHTML.replace(/<span class="(.+?)">(.+?)<\/span>/g,"$2"),n.innerHTML=replaceAll(n.innerHTML,"<div><br><br><br></div>","<br><br><br>"),n.innerHTML=replaceAll(n.innerHTML,"<div><br><br></div>","<br><br>"),n.innerHTML=replaceAll(n.innerHTML,"<div><br></div>","<br>");let o=n.innerText,r="";if(t.length>1){let e=t[t.length-1].length;e>0&&(r=o.slice(-e),o=o.slice(0,-e))}if(o.length>0){let t="\n";o.includes("\n\n")&&(t="\n\n");let n=o.split(t);for(var e=0;e<n.length;++e)0!=e?gametext_arr.push(t+n[e]):gametext_arr.push(n[e])}""!=r&&(gametext_arr.length>0&&"\n"==gametext_arr[gametext_arr.length-1]?gametext_arr[gametext_arr.length-1]+=r:gametext_arr.push(r)),render_gametext(),console.log("Merged edit field. Parts:"+gametext_arr.length)}null!=prev_hl_chunk&&(prev_hl_chunk.classList.remove("hlchunk"),prev_hl_chunk=null)}}function concat_gametext(e=!1,t="",n="",o="",r=!1){let s="";for(let e=0;e<gametext_arr.length;++e){let t=gametext_arr[e];r&&(t=escapeHtml(t)),""==t.trim()||"\n"==t.trim()?s+=t:s+=n+t+o}if(r){if(s=s.replace(/\[&lt;\|p\|.+?\|p\|&gt;\]/g,(function(e){return unescapeHtml(e)})),s=s.replace(/\[&lt;\|d\|.+?\|d\|&gt;\]/g,(function(e){return unescapeHtml(e)})),s=s.replace(/\[&lt;\|.+?\|&gt;\]/g,(function(e){return unescapeHtml(e)})),s=s.replace(/\n\n&gt; /g,(function(e){return unescapeHtml(e)})),3==localsettings.opmode&&""!=localsettings.chatname&&""!=localsettings.chatopponent){s=replaceAll(s,escapeHtml(localsettings.chatname),localsettings.chatname);var l=new RegExp("\n(?!"+localsettings.chatname+").+?: ","gi");s=s.replace(l,(function(e){return unescapeHtml(e)}))}if(4==localsettings.opmode&&""!=localsettings.instruct_starttag&&""!=localsettings.instruct_endtag){let e=escapeHtml(get_instruct_starttag(!1)),t=escapeHtml(get_instruct_endtag(!1));s=replaceAll(s,e,get_instruct_starttag(!1)),s=replaceAll(s,t,get_instruct_endtag(!1))}}return e&&(s=s.replace(/\[<\|p\|.+?\|p\|>\]/g,t),s=s.replace(/\[<\|d\|.+?\|d\|>\]/g,t),s=s.replace(/\[<\|.+?\|>\]/g,"")),s}function migrate_old_images_in_gametext(){let e=concat_gametext(!1,"","","",!1);if(!/\[<\|p\|.+?\|p\|>\]/.test(e)&&!/\[<\|d\|.+?\|d\|>\]/.test(e)&&(/<\|p\|.+?\|p\|>/.test(e)||/<\|d\|.+?\|d\|>/.test(e))){console.log("Migrating old images from saved story");for(let e=0;e<gametext_arr.length;++e)gametext_arr[e]=gametext_arr[e].replace(/<\|p\|.+?\|p\|>/g,(function(e){return"["+e+"]"})),gametext_arr[e]=gametext_arr[e].replace(/<\|d\|.+?\|d\|>/g,(function(e){return"["+e+"]"}))}}function render_gametext(e=!1){if(document.getElementById("gametext").contentEditable=document.getElementById("allowediting").checked&&""==pending_response_id,2==localsettings.opmode?(document.getElementById("inputrow").classList.add("show_mode"),localsettings.adventure_is_action?(document.getElementById("adventure_mode_txt").innerText="Action",document.getElementById("adventure_mode_img").classList.add("input_action"),document.getElementById("adventure_mode_img").classList.remove("input_story")):(document.getElementById("adventure_mode_txt").innerText="Story",document.getElementById("adventure_mode_img").classList.remove("input_action"),document.getElementById("adventure_mode_img").classList.add("input_story")),document.getElementById("btnmode").classList.remove("hidden")):(document.getElementById("inputrow").classList.remove("show_mode"),document.getElementById("btnmode").classList.add("hidden")),0==gametext_arr.length&&""==synchro_pending_stream&&""==pending_response_id){if(null==perfdata)document.getElementById("gametext").innerHTML='Welcome to <span class="color_cyan">KoboldAI Lite</span>!<br>You are in <span class="color_red">Offline Mode</span>.<br>You will still be able to load and edit stories, but not generate new text.';else{let e="";e=""!=custom_kobold_endpoint?'<br>You\'re using the custom KoboldAI endpoint at <span class="color_orange">'+custom_kobold_endpoint+"</span>":""!=custom_oai_key?"<br>You're using the OpenAI API":""!=custom_scale_key?"<br>You're using the Spellbook by Scale AI API":""!=custom_claude_key?"<br>You're using the Claude API":'<br>There are <span class="color_orange">'+selected_models.reduce(((e,t)=>e+t.count),0)+'</span> <a class="color_green" href="#" onclick="get_and_show_workers()">volunteer(s)</a> running selected models with a total queue length of <span class="color_orange">'+selected_models.reduce(((e,t)=>e+t.queued),0)+"</span> tokens",document.getElementById("gametext").innerHTML='Welcome to <span class="color_cyan">KoboldAI Lite</span>!<br>You are using the models <span class="color_green">'+selected_models.reduce(((e,t)=>e+(""==e?"":", ")+t.name),"")+"</span>"+(0==selected_workers.length?"":" (Pinned to "+selected_workers.length+" worker IDs)")+"."+e+'.<br><br>Enter a prompt below to begin!<br>Or, <a href="#" class="color_blueurl" onclick="display_scenarios()">select a Quick Start Scenario by clicking here.</a><br>'}document.getElementById("allowediting").checked&&(document.getElementById("allowediting").checked=!1,toggle_editable())}else{let e="";if(e=document.getElementById("allowediting").checked?concat_gametext(!1,"",'<span class="txtchunk">',"</span>",!0):concat_gametext(!1,"","","",!0),""!=synchro_pending_stream&&(e+='<span class="color_yellow">'+escapeHtml(pending_context_preinjection)+escapeHtml(synchro_pending_stream)+"</span>"),4!=localsettings.opmode||document.getElementById("allowediting").checked)e=replaceAll(e,get_instruct_starttag(!0),'<span 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8%;"/>'),e=replaceAll(e,"%SpcEtg%",'</span><hr class="hr_instruct"><img src="'+niko_square+'" style="padding:3px 6px 3px 3px;border-radius: 8%;"/>')}if(3==localsettings.opmode){let n="\n"+localsettings.chatname+": ";var t=new RegExp("\n(?!"+localsettings.chatname+").+?: ","gi");let o={},r=0;e=e.replace(t,(function(e){let t=escapeHtml(e),n=t.trim();return null==o[n]&&(o[n]=GetUniqueColor(r),++r),'<span class="'+o[n]+'">'+t+"</span>"})),e=replaceAll(e,n,'<span class="color_blue">'+escapeHtml(n)+"</span>")}2==localsettings.opmode&&(e=e.replace(/\n\n\> .+?\n/g,(function(e){return'<span class="color_green">'+e+"</span>"}))),e=e.replace(/\[<\|p\|.+?\|p\|>\]/g,(function(e){let t=e.substring(5,e.length-5);return t=render_image_html("",t),t})),e=e.replace(/\[<\|d\|.+?\|d\|>\]/g,(function(e){let t=e.substring(5,e.length-5);return 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if(0==selected_models.length&&0==selected_workers.length){document.getElementById("btnsend").disabled=!0,document.getElementById("btnsend").classList.add("wait"),document.getElementById("btnsend").classList.remove("btn-primary"),document.getElementById("btnsend").innerHTML="No AI<br>Loaded";let e='There are <span class="color_orange">'+perfdata.worker_count+'</span> total <a class="color_green" href="#" onclick="get_and_show_workers()">volunteer(s)</a> in the KoboldAI Horde, and <span class="color_orange">'+perfdata.queued_requests+'</span> request(s) in queues.<br>A total of <span class="color_orange">'+perfdata.past_minute_tokens+"</span> tokens were generated in the last minute.<br><br>";document.getElementById("gametext").innerHTML='Welcome to <span class="color_cyan">KoboldAI Lite</span>!<br><br>'+e+'<a href="#" class="color_blueurl" onclick="display_models()">Please select an AI model to use!</a><br>',document.getElementById("fvico").href=favivon_normal}else if(""==pending_response_id)document.getElementById("btnsend").disabled=!1,document.getElementById("btnsend").classList.remove("wait"),document.getElementById("btnsend").classList.add("btn-primary"),document.getElementById("btnsend").innerHTML="Submit",document.getElementById("fvico").href=favivon_normal;else{document.getElementById("btnsend").disabled=!0,document.getElementById("btnsend").classList.add("wait"),document.getElementById("btnsend").classList.remove("btn-primary");let e='<div class="outerloader"><div id="outerloadernum" class="outerloadernum"></div><div id="maintxtloader" class="innerloader"></div></div>';document.getElementById("btnsend").innerHTML!=e&&(document.getElementById("btnsend").innerHTML=e),document.getElementById("fvico").href=favicon_busy}let n=3==localsettings.opmode&&localsettings.enhanced_chat_ui||4==localsettings.opmode&&localsettings.enhanced_instruct_ui,o=!!document.getElementById("allowediting").checked;if(localsettings.enhanced_chat_ui&&!o&&n){let e=0==gametext_arr.length?document.getElementById("gametext").innerHTML:concat_gametext(!1,"","","",!0);if(3==localsettings.opmode?render_enhanced_chat(e):4==localsettings.opmode&&render_enhanced_chat_instruct(e),""==pending_response_id)document.getElementById("chatistyping").classList.add("hidden");else{let e=pending_context_preinjection&&pending_context_preinjection.includes(":")?pending_context_preinjection.split(":")[0]:"The AI";document.getElementById("chataityping").innerText=e+" is typing...",document.getElementById("chatistyping").classList.remove("hidden")}document.getElementById("chat_msg_send_btn").disabled=document.getElementById("btnsend").disabled,document.getElementById("enhancedchatinterface").classList.remove("hidden"),document.getElementById("normalinterface").classList.add("hidden")}else document.getElementById("enhancedchatinterface").classList.add("hidden"),document.getElementById("normalinterface").classList.remove("hidden");document.getElementById("btnautogenmem").disabled=document.getElementById("btnsend").disabled,localsettings.persist_session&&autosave(),0==e&&localsettings.autoscroll&&(document.getElementById("gametext").scrollTop=document.getElementById("gametext").scrollHeight,document.getElementById("chat_msg_body").scrollTop=document.getElementById("chat_msg_body").scrollHeight),idle_timer=0,document.getElementById("token-budget").innerText=last_token_budget}function render_enhanced_chat(e){var t=document.getElementById("chat_msg_body");if(!t)return;let n="",o=!1;var r=new RegExp("(?!"+localsettings.chatname+").+?: ","gi"),s=new RegExp("\\|[d|p]\\|>(?!"+localsettings.chatname+").+?\\: ","gi");let l=[];e=(e=e.replace(s,(function(e){return e.substring(0,4)+"\n"+e.substring(4)}))).split("\n");localsettings.chatname;for(var a=new RegExp("("+localsettings.chatname+"): ","gi"),i=0;i<e.length;++i){let t=e[i];var c=t.match(r),d=t.match(a);null!=t&&(null!=d&&d.length>0?(o=!0,l.push({name:d[0].substring(0,d[0].length-2),msg:t.split(d[0])[1],myturn:o})):null!=c&&c.length>0?(o=!1,l.push({name:c[0].substring(0,c[0].length-2),msg:t.split(c[0])[1],myturn:o})):0==l.length?""!=t.trim()&&l.push({name:"",msg:t,myturn:o}):l[l.length-1].msg+="<br>"+t)}let m={},u=0;for(i=0;i<l.length;++i){let e=l[i];if(e.msg&&""!=e.msg&&(e.msg=e.msg.replace(/\[<\|p\|.+?\|p\|>\]/g,(function(e){let t=e.substring(5,e.length-5);return t=render_image_html("",t,!1),t})),e.msg=e.msg.replace(/\[<\|d\|.+?\|d\|>\]/g,(function(e){let t=e.substring(5,e.length-5);return t=render_image_html(t,"",!1),t})),e.msg=e.msg.replace(/\[<\|.+?\|>\]/g,""),e.msg=e.msg.replace(/\*(\S[^*]+\S)\*/g,"<em style='opacity:0.7'>$1</em>")),e.myturn){n+='<div class="chat_outgoing_msg"><div class="chat_sent_msg"><p>'+(""!=e.name?'<span style="font-weight: bolder;color:#15e4c8b9;">'+escapeHtml(e.name)+"</span><br>":"")+e.msg+"</p></div></div>"}else{let t=escapeHtml(e.name),o=t.trim();null==m[o]&&(m[o]=GetUniqueColor(u),++u),n+='<div class="incoming_msg"><div class="chat_received_msg"><div class="chat_received_withd_msg"><p>'+(""!=e.name?"<span class='"+m[o]+'\' style="font-weight: bolder;">'+t+"</span><br>":"")+e.msg+"</p></div></div></div>"}}""!=synchro_pending_stream&&(n+='<div class="incoming_msg"><div class="chat_received_msg"><div class="chat_received_withd_msg"><p><span class="color_yellow">'+escapeHtml(pending_context_preinjection)+escapeHtml(synchro_pending_stream)+"</span></p></div></div></div>"),t.innerHTML=n}function render_enhanced_chat_instruct(e){var t=document.getElementById("chat_msg_body");if(!t)return;let n=get_instruct_starttag(),o=get_instruct_endtag(),r="</p></div></div></div><div class=\"incoming_msg\"><div class=\"chat_received_msg\" style='width:100%'><div class=\"chat_received_withd_msg\" style='width:100%;background-color:rgba(20, 40, 40, 0.8)'><p style='opacity:.8'>"+e.replaceAll(n+"\n",n).replaceAll(o+"\n",o).replaceAll(n+" ",n).replaceAll(o+" ",o);if(r.endsWith(o)&&(r=r.slice(0,-o.length)),localsettings.instruct_has_markdown){r=r.replaceAll(/\*(\S[^*]+\S)\*/g,"<em style='opacity:0.7'>$1</em>"),r=r.replaceAll(/&quot;(.*?)&quot;/g,"<em style='color:rgba(150, 150, 200, 1)'>\"$1\"</em>");let e=r.split(/(```.*?\n[\s\S]*?```)/gs);for(var s=0;s<e.length;s++)e[s].startsWith("```")?e[s]=e[s].replaceAll(/```.*?\n([\s\S]*?)```/gs,"</p><pre style='margin:0px 100px 0px 20px;background-color:black;color:rgba(180, 35, 40, 1)'>$1</pre><p>"):e[s]=e[s].replaceAll("```","`").replaceAll("``","`").replaceAll(/`(.*?)`/g,"<code style='background-color:black'>$1</code>");r=e.join("")}r=r.replaceAll(n,'</p></div></div></div><div class="incoming_msg"><div class="chat_received_msg" style=\'width:100%\'><div class="chat_received_withd_msg" style=\'width:100%\'><p>').replaceAll(o,'</p></div></div></div><div class="incoming_msg"><div class="chat_received_msg" style=\'width:100%\'><div class="chat_received_withd_msg" style=\'width:100%;background-color:rgba(20, 20, 40, 1)\'><p>'),t.innerHTML=r.replaceAll("\r\n","<br>").replaceAll("\n","<br>")}function chat_handle_typing(e){var t=(e=e||window.event).keyCode||e.which;if(!e.shiftKey&&13==t){let t=!0;document.getElementById("cht_inp").value;t&&(e.preventDefault(),document.getElementById("btnsend").disabled||chat_submit_generation())}}function chat_submit_generation(){document.getElementById("input_text").value=document.getElementById("cht_inp").value,submit_generation(),document.getElementById("cht_inp").value=""}function chat_toggle_actionmenu(){var e=document.getElementById("actionmenu2");e.classList.contains("hidden")?e.classList.remove("hidden"):e.classList.add("hidden")}function autosave(){if(localStorage.setItem((localmode?"e_":"")+"kaihordewebui_settings",JSON.stringify(localsettings)),localsettings.persist_session){let e=generate_compressed_story();localStorage.setItem((localmode?"e_":"")+"kaihordewebui_story",e)}console.log("autosave done")}function btn_adventure_mode(){localsettings.adventure_is_action=!localsettings.adventure_is_action,render_gametext()}function btn_memory(){document.getElementById("memorycontainer").classList.remove("hidden"),document.getElementById("memorytext").value=current_memory,document.getElementById("anotetext").value=current_anote,document.getElementById("anotetemplate").value=current_anotetemplate,document.getElementById("anote_strength").value=anote_strength,document.getElementById("extrastopseq").value=extrastopseq}function toggle_wi_sk(e){var t=current_wi[e];t.selective=!t.selective;var n=document.getElementById("wiskt"+e),o=document.getElementById("wikeysec"+e);t.selective?(n.classList.add("witoggleron"),n.classList.remove("witoggleroff"),o.classList.remove("hidden")):(n.classList.remove("witoggleron"),n.classList.add("witoggleroff"),o.classList.add("hidden"))}function toggle_wi_ck(e){var t=current_wi[e];t.constant=!t.constant;var n=document.getElementById("wickt"+e);t.constant?(n.classList.add("witoggleron"),n.classList.remove("witoggleroff")):(n.classList.remove("witoggleron"),n.classList.add("witoggleroff"))}function del_wi(e){save_wi();current_wi[e];current_wi.splice(e,1),btn_wi()}function add_wi(){save_wi();current_wi.push({key:"",keysecondary:"",content:"",comment:"",folder:null,selective:!1,constant:!1}),btn_wi()}function save_wi(){for(var e=0;e<current_wi.length;++e)current_wi[e].key=document.getElementById("wikey"+e).value,current_wi[e].keysecondary=document.getElementById("wikeysec"+e).value,current_wi[e].content=document.getElementById("wival"+e).value;localsettings.case_sensitive_wi=!!document.getElementById("case_sensitive_wi").checked}let backup_wi_obj=[];function revert_wi(){current_wi=JSON.parse(JSON.stringify(backup_wi_obj))}function backup_wi(){backup_wi_obj=JSON.parse(JSON.stringify(current_wi))}function btn_wi(){document.getElementById("case_sensitive_wi").checked=!!localsettings.case_sensitive_wi,document.getElementById("wicontainer").classList.remove("hidden");let e=document.getElementById("wilist");selectionhtml='<table style="border-collapse: separate; border-spacing: 1.5pt;">';for(var t=0;t<current_wi.length;++t){var n=current_wi[t],o=escapeHtml(n.key),r=escapeHtml(n.content),s=n.keysecondary;selectionhtml+='<tr id="wirow'+t+'"><td class="col-8" style="font-size: 10px;"><button type="button" class="btn btn-danger widelbtn" id="widel'+t+'" onclick="return del_wi('+t+')">X</button></td><td class="col-6">\n\t\t<input class="form-control wiinputkey" id="wikey'+t+'" placeholder="Key(s)" value="'+o+'">\n\t\t<input class="form-control wiinputkey '+(n.selective?"":"hidden")+'" id="wikeysec'+t+'" placeholder="Sec. Key(s)" value="'+s+'"></td>\n\t\t<td class="col-10">\n\t\t<textarea class="form-control wiinputval" style="line-height:1.1" id="wival'+t+'" placeholder="What To Remember" rows="3">'+r+'</textarea>\n\t\t</td>\n\t\t<td>\n\t\t\t<a id="wiskt'+t+'" href="#" class='+(n.selective?"witoggleron":"witoggleroff")+' title="Toggle Selective Key mode (if enabled, this world info entry will be included in memory only if at least one PRIMARY KEY and at least one SECONDARY KEY are both present in the story)" onclick="return toggle_wi_sk('+t+')">📑</a>\n\t\t\t<a id="wickt'+t+'" href="#" class='+(n.constant?"witoggleron":"witoggleroff")+' title="Toggle Constant Key mode (if enabled, this world info entry will always be included in memory)" onclick="return toggle_wi_ck('+t+')">📌</a>\n\t\t\t</td>\n\t\t</tr>\n\t\t'}0==current_wi.length&&(selectionhtml='<div class="aidgpopuplistheader anotelabel">No world info.<br>Click [+] to add a new entry.</div>'),selectionhtml+="</table>",e.innerHTML=selectionhtml}var backLongPressTimer=null;function btn_back_longpress_start(){backLongPressTimer=setTimeout((()=>{if(console.log("Clear story"),""==pending_response_id&&gametext_arr.length>0){for(;gametext_arr.length>0;)if(""!=retry_prev_text)redo_prev_text=gametext_arr.pop(),gametext_arr.push(retry_prev_text),retry_prev_text="";else{let e=gametext_arr.pop();redo_arr.push(e)}render_gametext()}}),3e3)}function btn_back_longpress_end(){clearTimeout(backLongPressTimer)}function btn_back(){if(""==pending_response_id&&gametext_arr.length>0){if(""!=retry_prev_text)redo_prev_text=gametext_arr.pop(),gametext_arr.push(retry_prev_text),retry_prev_text="";else{let e=gametext_arr.pop();redo_arr.push(e)}render_gametext()}}var redoLongPressTimer=null;function btn_redo_longpress_start(){redoLongPressTimer=setTimeout((()=>{if(console.log("Redo All story"),""==pending_response_id&&redo_arr.length>0){for(;redo_arr.length>0;){retry_prev_text="";let e=redo_arr.pop();gametext_arr.push(e)}btn_redo(),render_gametext()}}),3e3)}function btn_redo_longpress_end(){clearTimeout(redoLongPressTimer)}function btn_redo(){if(""==pending_response_id)if(redo_arr.length>0){retry_prev_text="";let e=redo_arr.pop();gametext_arr.push(e),render_gametext()}else""!=redo_prev_text&&(retry_prev_text=gametext_arr.pop(),gametext_arr.push(redo_prev_text),redo_prev_text="",render_gametext())}function btn_retry(){if(""==pending_response_id&&gametext_arr.length>0){let e=document.getElementById("input_text").value;document.getElementById("input_text").value="";let t=gametext_arr[gametext_arr.length-1];redo_prev_text="",retry_prev_text="",gametext_arr.pop(),submit_generation(),retry_prev_text=t,redo_arr=[],document.getElementById("input_text").value=e}}function toggleNavWithoutBootstrapJS(){var e=document.getElementById("navbarNavDropdown");e.classList.contains("collapse")?e.classList.remove("collapse"):e.classList.add("collapse")}const clamp=(e,t,n)=>Math.min(Math.max(e,t),n),cleannum=function(e,t,n){let o=isNaN(e)?0:e;return clamp(o,t,n)}</script>
 </head>
 <body>
 <div class="container maincontainer">
@@ -134,8 +134,7 @@ Kobold Lite is under the AGPL v3.0 License for the purposes of koboldcpp. Please
 <div class=lastreq id=lastreq style=color:#999><span class=color_gray>Avoid sending privacy sensitive information. <a href=# onclick=explain_horde()>Click here for more info</a>.</span></div>
 </div>
 <div id=enhancedchatinterface class="chat_mesgs hidden">
-<div id=chat_msg_body class=chat_msg_history>
-</div>
+<div id=chat_msg_body class=chat_msg_history></div>
 <div class=hidden id=chatistyping style=text-align:right;font-size:13px;color:#999;padding-bottom:3px><div style=padding-bottom:2px id=chataityping>The AI is typing...</div><div style=padding-top:2px;text-align:right class="dot-flashing flex flex-push-right"></div></div>
 <div class="flex hidden" id=actionmenu2>
 <div id=actionmenuitems2 class="box flex-push-right" style=margin-bottom:2px>
@@ -310,7 +309,7 @@ Model Choice:<br>
 <option value=claude-2.0>claude-2.0</option>
 </select>
 <input type=checkbox id=clauderenamecompat>
-<div class=box-label title="Rename User and Bot tags to work with claude">Claude Compatibility Rename Fix</div>
+<div class=box-label title="Rename User and Bot tags to work with claude, force inject them otherwise">Claude Compatibility Rename Fix</div>
 </div>
 <div class=popupfooter>
 <button type=button class="btn btn-primary" onclick=connect_custom_endpoint()>Connect</button>
@@ -445,7 +444,7 @@ Unsaved data will be lost.<br><br>
 <tr>
 <td><input placeholder=0 value=0 id=rep_pen_range title="Repetition Penalty Range"></td>
 <td><input placeholder=0 value=0 id=rep_pen_slope title="Repetition Penalty Slope"></td>
-<td><input placeholder=CSV id=sampler_order style=width:80px title="Valid values are: 0=top_k, 1=top_a, 2=top_p, 3=tfs, 4=typ, 5=temp, 6=rep_pen" onblur=validate_samplers()></td>
+<td><input placeholder=CSV id=sampler_order style=width:70px title="Valid values are: 0=top_k, 1=top_a, 2=top_p, 3=tfs, 4=typ, 5=temp, 6=rep_pen" onblur=validate_samplers()></td>
 </tr>
 </table>
 </div>
@@ -508,8 +507,8 @@ Unsaved data will be lost.<br><br>
 <th>AI Name <span class=helpicon>?<span class=helptext>The name of the person you want to chat with. Multiple opponents can be specified, creating a group chat, separate their names with ||$||</span></span></th>
 </tr>
 <tr>
-<td><input class="settinglabel mininiput" placeholder="(Enter Name)" id=chatname title="The name that you will be chatting as"></td>
-<td><input class="settinglabel mininiput" placeholder=(Auto) id=chatopponent title="The name of the person you want to chat with"></td>
+<td><input class="settinglabel miniinput" placeholder="(Enter Name)" id=chatname title="The name that you will be chatting as"></td>
+<td><input class="settinglabel miniinput" placeholder=(Auto) id=chatopponent title="The name of the person you want to chat with"></td>
 </tr>
 </table>
 <div class=settinglabel>
@@ -536,10 +535,14 @@ Unsaved data will be lost.<br><br>
 <th>End Seq.<span class=helpicon>?<span class=helptext>The sequence to end an instruction prompt</span></span></th>
 </tr>
 <tr>
-<td><input class="settinglabel mininiput" placeholder="\\n### Instruction:\\n" id=instruct_starttag title="The sequence to start an instruction prompt"></td>
-<td><input class="settinglabel mininiput" placeholder="\\n### Response:\\n" id=instruct_endtag title="The sequence to end an instruction prompt"></td>
+<td><input class="settinglabel miniinput" placeholder="\\n### Instruction:\\n" id=instruct_starttag title="The sequence to start an instruction prompt"></td>
+<td><input class="settinglabel miniinput" placeholder="\\n### Response:\\n" id=instruct_endtag title="The sequence to end an instruction prompt"></td>
 </tr>
 </table>
+<div class=settinglabel>
+<div class="justifyleft settingsmall" title="Switches to a UI style more suitable for RP. Enabling markdown together with this is recommended.">Aesthetic Instruct UI </div>
+<input type=checkbox id=enhanced_instruct_ui style="margin:0 5.5px">
+</div>
 <div class="justifyleft settingsmall">Enable Markdown <span class=helpicon>?<span class=helptext>Allows the UI to use markdown formatting such as quotes and code blocks.</span></span> </div>
 <input type=checkbox id=instruct_has_markdown style="margin:0 0 0">
 </div>
@@ -627,6 +630,7 @@ A/N Strength
 <option value=480>Weak</option>
 <option value=320>Medium</option>
 <option value=160>Strong</option>
+<option value=0>Immediate</option>
 </select>
 <br><br>
 <div class="justifyleft settinglabel">Extra Stopping Sequence (Kobold API Only) <span class=helpicon>?<span class=helptext>Triggers the text generator to stop generating early if this sequence appears, in addition to default stop sequences. If you want multiple sequences, separate them with the following delimiter: ||$||</span></span></div>
diff --git a/koboldcpp.py b/koboldcpp.py
index 0db372a320ff0bdb64899d5df2403e56311b0ee9..ab3ce85ec959e971f510dc9a51eebab335a4471b 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -23,6 +23,7 @@ class load_model_inputs(ctypes.Structure):
                 ("batch_size", ctypes.c_int),
                 ("f16_kv", ctypes.c_bool),
                 ("low_vram", ctypes.c_bool),
+                ("use_mmq", ctypes.c_bool),
                 ("executable_path", ctypes.c_char_p),
                 ("model_filename", ctypes.c_char_p),
                 ("lora_filename", ctypes.c_char_p),
@@ -65,7 +66,7 @@ class generation_inputs(ctypes.Structure):
 
 class generation_outputs(ctypes.Structure):
     _fields_ = [("status", ctypes.c_int),
-                ("text", ctypes.c_char * 16384)]
+                ("text", ctypes.c_char * 24576)]
 
 handle = None
 
@@ -89,29 +90,30 @@ def pick_existant_file(ntoption,nonntoption):
 lib_default = pick_existant_file("koboldcpp.dll","koboldcpp.so")
 lib_failsafe = pick_existant_file("koboldcpp_failsafe.dll","koboldcpp_failsafe.so")
 lib_openblas = pick_existant_file("koboldcpp_openblas.dll","koboldcpp_openblas.so")
-lib_openblas_noavx2 = pick_existant_file("koboldcpp_openblas_noavx2.dll","koboldcpp_openblas_noavx2.so")
+lib_noavx2 = pick_existant_file("koboldcpp_noavx2.dll","koboldcpp_noavx2.so")
 lib_clblast = pick_existant_file("koboldcpp_clblast.dll","koboldcpp_clblast.so")
 lib_cublas = pick_existant_file("koboldcpp_cublas.dll","koboldcpp_cublas.so")
 
 
 def init_library():
     global handle
-    global lib_default,lib_failsafe,lib_openblas,lib_openblas_noavx2,lib_clblast,lib_cublas
+    global lib_default,lib_failsafe,lib_openblas,lib_noavx2,lib_clblast,lib_cublas
 
     libname = ""
-    use_blas = False # if true, uses OpenBLAS for acceleration. libopenblas.dll must exist in the same dir.
+    use_openblas = False # if true, uses OpenBLAS for acceleration. libopenblas.dll must exist in the same dir.
     use_clblast = False #uses CLBlast instead
     use_cublas = False #uses cublas instead
-    use_noavx2 = False #uses openblas with no avx2 instructions
+    use_noavx2 = False #uses no avx2 instructions
+    use_failsafe = False #uses no intrinsics, failsafe mode
     if args.noavx2:
         use_noavx2 = True
-        if not file_exists(lib_openblas_noavx2) or (os.name=='nt' and not file_exists("libopenblas.dll")):
-            print("Warning: OpenBLAS library file not found. Non-BLAS library will be used.")
-        elif args.noblas:
+        if not file_exists(lib_noavx2):
+            print("Warning: NoAVX2 library file not found. Failsafe library will be used.")
+        elif (args.noblas and args.nommap):
+            use_failsafe = True
             print("!!! Attempting to use FAILSAFE MODE !!!")
         else:
-            use_blas = True
-            print("Attempting to use non-avx2 compatibility library with OpenBLAS. A compatible libopenblas will be required.")
+            print("Attempting to use non-avx2 compatibility library.")
     elif args.useclblast:
         if not file_exists(lib_clblast) or (os.name=='nt' and not file_exists("clblast.dll")):
             print("Warning: CLBlast library file not found. Non-BLAS library will be used.")
@@ -130,22 +132,22 @@ def init_library():
         elif args.noblas:
             print("Attempting to library without OpenBLAS.")
         else:
-            use_blas = True
+            use_openblas = True
             print("Attempting to use OpenBLAS library for faster prompt ingestion. A compatible libopenblas will be required.")
             if sys.platform=="darwin":
                 print("Mac OSX note: Some people have found Accelerate actually faster than OpenBLAS. To compare, run Koboldcpp with --noblas instead.")
 
     if use_noavx2:
-        if use_blas:
-            libname = lib_openblas_noavx2
-        else:
+        if use_failsafe:
             libname = lib_failsafe
+        else:
+            libname = lib_noavx2
     else:
         if use_clblast:
             libname = lib_clblast
         elif use_cublas:
             libname = lib_cublas
-        elif use_blas:
+        elif use_openblas:
             libname = lib_openblas
         else:
             libname = lib_default
@@ -178,6 +180,7 @@ def load_model(model_filename):
     inputs.max_context_length = maxctx #initial value to use for ctx, can be overwritten
     inputs.threads = args.threads
     inputs.low_vram = (True if (args.usecublas and "lowvram" in args.usecublas) else False)
+    inputs.use_mmq = (True if (args.usecublas and "mmq" in args.usecublas) else False)
     inputs.blasthreads = args.blasthreads
     inputs.f16_kv = True
     inputs.use_mmap = (not args.nommap)
@@ -229,12 +232,17 @@ def load_model(model_filename):
     return ret
 
 def generate(prompt,max_length=20, max_context_length=512, temperature=0.8, top_k=120, top_a=0.0, top_p=0.85, typical_p=1.0, tfs=1.0, rep_pen=1.1, rep_pen_range=128, mirostat=0, mirostat_tau=5.0, mirostat_eta=0.1, sampler_order=[6,0,1,3,4,2,5], seed=-1, stop_sequence=[], stream_sse=False):
+    global maxctx
     inputs = generation_inputs()
     outputs = ctypes.create_unicode_buffer(ctypes.sizeof(generation_outputs))
     inputs.prompt = prompt.encode("UTF-8")
     if max_length >= max_context_length:
         max_length = max_context_length-1
     inputs.max_context_length = max_context_length   # this will resize the context buffer if changed
+    global showmaxctxwarning
+    if showmaxctxwarning and max_context_length > maxctx:
+        print(f"\n(Warning! Request max_context_length={max_context_length} exceeds allocated context size of {maxctx}. Consider launching with increased --contextsize to avoid errors. This message will only show once per session.)")
+        showmaxctxwarning = False
     inputs.max_length = max_length
     inputs.temperature = temperature
     inputs.top_k = top_k
@@ -295,9 +303,10 @@ maxhordectx = 1024
 maxhordelen = 256
 modelbusy = threading.Lock()
 defaultport = 5001
-KcppVersion = "1.37.1"
+KcppVersion = "1.40.1"
 showdebug = True
 showsamplerwarning = True
+showmaxctxwarning = True
 exitcounter = 0
 
 class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
@@ -393,6 +402,7 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
 
         current_token = 0
 
+        incomplete_token_buffer = bytearray()
         while not handle.has_finished():
             if current_token < handle.get_stream_count():
                 token = handle.new_token(current_token)
@@ -402,10 +412,14 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
 
                 current_token += 1
 
-                tokenStr = ctypes.string_at(token).decode("UTF-8","ignore")
-                event_data = {"token": tokenStr}
-                event_str = json.dumps(event_data)
-                await self.send_sse_event("message", event_str)
+                newbyte = ctypes.string_at(token)
+                incomplete_token_buffer += bytearray(newbyte)
+                tokenStr = incomplete_token_buffer.decode("UTF-8","ignore")
+                if tokenStr!="":
+                    incomplete_token_buffer.clear()
+                    event_data = {"token": tokenStr}
+                    event_str = json.dumps(event_data)
+                    await self.send_sse_event("message", event_str)
 
             await asyncio.sleep(0)
 
@@ -481,7 +495,7 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
             laste = handle.get_last_eval_time()
             lastc = handle.get_last_token_count()
             stopreason = handle.get_last_stop_reason()
-            response_body = (json.dumps({"last_process":lastp,"last_eval":laste,"last_token_count":lastc, "stop_reason":stopreason}).encode())
+            response_body = (json.dumps({"last_process":lastp,"last_eval":laste,"last_token_count":lastc, "stop_reason":stopreason, "idle":(0 if modelbusy.locked() else 1)}).encode())
 
         if response_body is None:
             self.send_response(404)
@@ -558,9 +572,12 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
                 newprompt = fullprompt
 
                 gen = asyncio.run(self.handle_request(genparams, newprompt, basic_api_flag, kai_sse_stream_flag))
+
                 try:
-                    self.send_response(200)
-                    self.end_headers()
+                    # Headers are already sent when streaming
+                    if not kai_sse_stream_flag:
+                        self.send_response(200)
+                        self.end_headers()
                     self.wfile.write(json.dumps(gen).encode())
                 except:
                     print("Generate: The response could not be sent, maybe connection was terminated?")
@@ -629,7 +646,7 @@ def RunServerMultiThreaded(addr, port, embedded_kailite = None):
             exitcounter = 999
             self.httpd.server_close()
 
-    numThreads = 6
+    numThreads = 8
     threadArr = []
     for i in range(numThreads):
         threadArr.append(Thread(i))
@@ -656,14 +673,13 @@ def show_new_gui():
         root.destroy()
         if not args.model_param:
             print("\nNo ggml model file was selected. Exiting.")
-            time.sleep(2)
+            time.sleep(3)
             sys.exit(2)
         return
 
     import customtkinter as ctk
-
     nextstate = 0 #0=exit, 1=launch, 2=oldgui
-    windowwidth = 520
+    windowwidth = 530
     windowheight = 500
     ctk.set_appearance_mode("dark")
     root = ctk.CTk()
@@ -684,13 +700,22 @@ def show_new_gui():
     tabcontentframe.grid_propagate(False)
 
     tabcontent = {}
-
+    lib_option_pairs = [
+        (lib_openblas, "Use OpenBLAS"),
+        (lib_clblast, "Use CLBlast"),
+        (lib_cublas, "Use CuBLAS"),
+        (lib_default, "Use No BLAS"),
+        (lib_noavx2, "NoAVX2 Mode (Old CPU)"),
+        (lib_failsafe, "Failsafe Mode (Old CPU)")]
+    openblas_option, clblast_option, cublas_option, default_option, noavx2_option, failsafe_option = (opt if file_exists(lib) or (os.name == 'nt' and file_exists(opt + ".dll")) else None for lib, opt in lib_option_pairs)
     # slider data
-    blasbatchsize_values = ["-1", "32", "64", "128", "256", "512", "1024"]
-    blasbatchsize_text = ["Don't Batch BLAS","32","64","128","256","512","1024"]
-    contextsize_text = ["512", "1024", "2048", "3072", "4096", "6144", "8192"]
-    runopts = ["Use OpenBLAS","Use CLBlast", "Use CuBLAS", "Use No BLAS","Use OpenBLAS (Old CPU, noavx2)","Failsafe Mode (Old CPU, noavx)"]
-
+    blasbatchsize_values = ["-1", "32", "64", "128", "256", "512", "1024", "2048"]
+    blasbatchsize_text = ["Don't Batch BLAS","32","64","128","256","512","1024","2048"]
+    contextsize_text = ["512", "1024", "2048", "3072", "4096", "6144", "8192", "12288", "16384"]
+    runopts = [opt for lib, opt in lib_option_pairs if file_exists(lib) or os.name == 'nt' and file_exists(opt + ".dll")]
+    antirunopts = [opt.replace("Use ", "") for lib, opt in lib_option_pairs if not file_exists(lib) or os.name == 'nt' and not file_exists(opt + ".dll")]
+    if not any(runopts):
+        show_gui_warning("No Backend Available")
     def tabbuttonaction(name):
         for t in tabcontent:
             if name == t:
@@ -756,6 +781,32 @@ def show_new_gui():
         button.grid(row=row+1, column=1, stick="nw")
         return
 
+    def show_tooltip(event, tooltip_text=None):
+        if hasattr(show_tooltip, "_tooltip"):
+            tooltip = show_tooltip._tooltip
+        else:
+            tooltip = ctk.CTkToplevel(root)
+            tooltip.configure(fg_color="#ffffe0")
+            tooltip.withdraw()
+            tooltip.overrideredirect(True)
+            tooltip_label = ctk.CTkLabel(tooltip, text=tooltip_text, text_color="#000000", fg_color="#ffffe0")
+            tooltip_label.pack(expand=True, padx=2, pady=1)
+            show_tooltip._tooltip = tooltip
+        x, y = root.winfo_pointerxy()
+        tooltip.wm_geometry(f"+{x + 10}+{y + 10}")
+        tooltip.deiconify()
+    def hide_tooltip(event):
+        if hasattr(show_tooltip, "_tooltip"):
+            tooltip = show_tooltip._tooltip
+            tooltip.withdraw()
+    def setup_backend_tooltip(parent):
+        num_backends_built = makelabel(parent, str(len(runopts)) + "/6", 5, 2)
+        num_backends_built.grid(row=1, column=2, padx=0, pady=0)
+        num_backends_built.configure(text_color="#00ff00")
+        # Bind the backend count label with the tooltip function
+        num_backends_built.bind("<Enter>", lambda event: show_tooltip(event, f"This is the number of backends you have built and available." + (f"\nMissing: {', '.join(antirunopts)}" if len(runopts) != 6 else "")))
+        num_backends_built.bind("<Leave>", hide_tooltip)
+
     # Vars - should be in scope to be used by multiple widgets
     gpulayers_var = ctk.StringVar(value="0")
     threads_var = ctk.StringVar(value=str(default_threads))
@@ -770,6 +821,7 @@ def show_new_gui():
     debugmode = ctk.IntVar()
 
     lowvram_var = ctk.IntVar()
+    mmq_var = ctk.IntVar()
 
     blas_threads_var = ctk.StringVar()
     blas_size_var = ctk.IntVar()
@@ -806,11 +858,13 @@ def show_new_gui():
     quick_tab = tabcontent["Quick Launch"]
 
     # gpu options
-    quick_gpu_layers_entry,quick_gpu_layers_label = makelabelentry(quick_tab,"GPU Layers:", gpulayers_var, 4, 50)
+    quick_gpu_layers_entry,quick_gpu_layers_label = makelabelentry(quick_tab,"GPU Layers:", gpulayers_var, 5, 50)
     quick_gpu_selector_label = makelabel(quick_tab, "GPU ID:", 3)
     quick_gpu_selector_box = ctk.CTkComboBox(quick_tab, values=["1","2","3"], width=60, variable=gpu_choice_var, state="readonly")
     CUDA_quick_gpu_selector_box = ctk.CTkComboBox(quick_tab, values=["1","2","3","All"], width=60, variable=gpu_choice_var, state="readonly")
-    quick_lowvram_box = makecheckbox(quick_tab,  "Low VRAM", lowvram_var, 5)
+    quick_lowvram_box = makecheckbox(quick_tab,  "Low VRAM", lowvram_var, 4,0)
+    quick_mmq_box = makecheckbox(quick_tab,  "Use QuantMatMul (mmq)", mmq_var, 4,1)
+
 
     def changerunmode(a,b,c):
         index = runopts_var.get()
@@ -836,9 +890,13 @@ def show_new_gui():
         if index == "Use CuBLAS":
             lowvram_box.grid(row=4, column=0, padx=8, pady=1,  stick="nw")
             quick_lowvram_box.grid(row=4, column=0, padx=8, pady=1,  stick="nw")
+            mmq_box.grid(row=4, column=1, padx=8, pady=1,  stick="nw")
+            quick_mmq_box.grid(row=4, column=1, padx=8, pady=1,  stick="nw")
         else:
             lowvram_box.grid_forget()
             quick_lowvram_box.grid_forget()
+            mmq_box.grid_forget()
+            quick_mmq_box.grid_forget()
 
         if index == "Use CLBlast" or index == "Use CuBLAS":
             gpu_layers_label.grid(row=5, column=0, padx = 8, pady=1, stick="nw")
@@ -856,19 +914,21 @@ def show_new_gui():
 
     runoptbox = ctk.CTkComboBox(quick_tab, values=runopts, width=180,variable=runopts_var, state="readonly")
     runoptbox.grid(row=1, column=1,padx=8, stick="nw")
-    runoptbox.set("Use OpenBLAS")
+    runoptbox.set(runopts[0]) # Set to first available option
+
+    # Tell user how many backends are available
+    setup_backend_tooltip(quick_tab)
 
     # threads
     makelabelentry(quick_tab, "Threads:" , threads_var, 8, 50)
 
     # blas batch size
-    makeslider(quick_tab, "BLAS Batch Size:", blasbatchsize_text, blas_size_var, 0, 6, 12, set=5)
+    makeslider(quick_tab, "BLAS Batch Size:", blasbatchsize_text, blas_size_var, 0, 7, 12, set=5)
 
     # quick boxes
     quick_boxes = {"Launch Browser": launchbrowser , "High Priority" : highpriority, "Streaming Mode":stream, "Use SmartContext":smartcontext, "Unban Tokens":unbantokens, "Disable MMAP":disablemmap,}
     for idx, name, in enumerate(quick_boxes):
         makecheckbox(quick_tab, name, quick_boxes[name], int(idx/2) +20, idx%2)
-
     # context size
     makeslider(quick_tab, "Context Size:", contextsize_text, context_var, 0, len(contextsize_text)-1, 30, set=2)
 
@@ -879,19 +939,24 @@ def show_new_gui():
     hardware_tab = tabcontent["Hardware"]
 
     # gpu options
-    gpu_layers_entry,gpu_layers_label = makelabelentry(hardware_tab,"GPU Layers:", gpulayers_var, 4, 50)
+    gpu_layers_entry,gpu_layers_label = makelabelentry(hardware_tab,"GPU Layers:", gpulayers_var, 5, 50)
     gpu_selector_label = makelabel(hardware_tab, "GPU ID:", 3)
     gpu_selector_box = ctk.CTkComboBox(hardware_tab, values=["1","2","3"], width=60, variable=gpu_choice_var, state="readonly")
     CUDA_gpu_selector_box = ctk.CTkComboBox(hardware_tab, values=["1","2","3", "All"], width=60, variable=gpu_choice_var, state="readonly")
-    lowvram_box = makecheckbox(hardware_tab,  "Low VRAM", lowvram_var, 5)
+    lowvram_box = makecheckbox(hardware_tab,  "Low VRAM", lowvram_var, 4,0)
+    mmq_box = makecheckbox(hardware_tab,  "Use QuantMatMul (mmq)", mmq_var, 4,1)
 
     # presets selector
     makelabel(hardware_tab, "Presets:", 1)
     runoptbox = ctk.CTkComboBox(hardware_tab, values=runopts,  width=180,variable=runopts_var, state="readonly")
     runoptbox.grid(row=1, column=1,padx=8, stick="nw")
-    runoptbox.set("Use OpenBLAS")
+    runoptbox.set(runopts[0]) # Set to first available option
     runopts_var.trace('w', changerunmode)
     changerunmode(1,1,1)
+
+    # Tell user how many backends are available
+    setup_backend_tooltip(hardware_tab)
+
     # threads
     makelabelentry(hardware_tab, "Threads:" , threads_var, 8, 50)
 
@@ -904,7 +969,7 @@ def show_new_gui():
     # blas thread specifier
     makelabelentry(hardware_tab, "BLAS threads:" , blas_threads_var, 11, 50)
     # blas batch size
-    makeslider(hardware_tab, "BLAS Batch Size:", blasbatchsize_text, blas_size_var, 0, 6, 12, set=5)
+    makeslider(hardware_tab, "BLAS Batch Size:", blasbatchsize_text, blas_size_var, 0, 7, 12, set=5)
     # force version
     makelabelentry(hardware_tab, "Force Version:" , version_var, 100, 50)
 
@@ -931,7 +996,7 @@ def show_new_gui():
     togglemiro(1,1,1)
 
     # context size
-    makeslider(tokens_tab, "Context Size:",contextsize_text, context_var, 0, 4, 20, set=2)
+    makeslider(tokens_tab, "Context Size:",contextsize_text, context_var, 0, len(contextsize_text)-1, 20, set=2)
 
 
     customrope_scale_entry, customrope_scale_label = makelabelentry(tokens_tab, "RoPE Scale:", customrope_scale)
@@ -1017,20 +1082,22 @@ def show_new_gui():
         gpuchoiceidx = 0
         if gpu_choice_var.get()!="All":
             gpuchoiceidx = int(gpu_choice_var.get())-1
-        if runopts_var.get() == runopts[1]:
+        if runopts_var.get() == "Use CLBlast":
             args.useclblast = [[0,0], [1,0], [0,1]][gpuchoiceidx]
-        if runopts_var.get() == runopts[2]:
+        if runopts_var.get() == "Use CuBLAS":
             if gpu_choice_var.get()=="All":
                 args.usecublas = ["lowvram"] if lowvram_var.get() == 1 else ["normal"]
             else:
                 args.usecublas = ["lowvram",str(gpuchoiceidx)] if lowvram_var.get() == 1 else ["normal",str(gpuchoiceidx)]
+            if mmq_var.get()==1:
+                args.usecublas.append("mmq")
         if gpulayers_var.get():
             args.gpulayers = int(gpulayers_var.get())
-        if runopts_var.get()==runopts[3]:
+        if runopts_var.get()=="Use No BLAS":
             args.noblas = True
-        if runopts_var.get()==runopts[4]:
+        if runopts_var.get()=="NoAVX2 Mode (Old CPU)":
             args.noavx2 = True
-        if runopts_var.get()==runopts[5]:
+        if runopts_var.get()=="Failsafe Mode (Old CPU)":
             args.noavx2 = True
             args.noblas = True
             args.nommap = True
@@ -1040,7 +1107,7 @@ def show_new_gui():
         args.blasbatchsize = int(blasbatchsize_values[int(blas_size_var.get())])
         args.forceversion = 0 if version_var.get()=="" else int(version_var.get())
 
-        args.mirostat = [int(mirostat_var.get()), float(mirostat_tau.get()), float(mirostat_eta.get())] if usemirostat.get()==1 else None
+        args.usemirostat = [int(mirostat_var.get()), float(mirostat_tau.get()), float(mirostat_eta.get())] if usemirostat.get()==1 else None
         args.contextsize = int(contextsize_text[context_var.get()])
 
         if customrope_var.get()==1:
@@ -1069,38 +1136,39 @@ def show_new_gui():
         stream.set(1 if "stream" in dict and dict["stream"] else 0)
         smartcontext.set(1 if "smartcontext" in dict and dict["smartcontext"] else 0)
         unbantokens.set(1 if "unbantokens" in dict and dict["unbantokens"] else 0)
-        runopts_var.set(runopts[0])
         if "useclblast" in dict and dict["useclblast"]:
-            runopts_var.set(runopts[1])
-            gpu_choice_var.set(str(["0 0", "1 0", "0 1"].index(str(dict["useclblast"][0]) + " " + str(dict["useclblast"][1])) + 1))
+            if clblast_option is not None:
+                runopts_var.set(clblast_option)
+                gpu_choice_var.set(str(["0 0", "1 0", "0 1"].index(str(dict["useclblast"][0]) + " " + str(dict["useclblast"][1])) + 1))
         elif "usecublas" in dict and dict["usecublas"]:
-            runopts_var.set(runopts[2])
-            if len(dict["usecublas"])==1:
-                lowvram_var.set(1 if dict["usecublas"][0]=="lowvram" else 0)
-            else:
+            if cublas_option is not None:
+                runopts_var.set(cublas_option)
                 lowvram_var.set(1 if "lowvram" in dict["usecublas"] else 0)
-                gpu_choice_var.set("1")
+                mmq_var.set(1 if "mmq" in dict["usecublas"] else 0)
+                gpu_choice_var.set("All")
                 for g in range(3):
                     if str(g) in dict["usecublas"]:
                         gpu_choice_var.set(str(g+1))
                         break
-        if "gpulayers" in dict and dict["gpulayers"]:
-            gpulayers_var.set(dict["gpulayers"])
-
-        if  "noavx2" in dict and "noblas" in dict and dict["noblas"] and dict["noavx2"]:
-            runopts_var.set(runopts[5])
+        elif  "noavx2" in dict and "noblas" in dict and dict["noblas"] and dict["noavx2"]:
+            if failsafe_option is not None:
+                runopts_var.set(failsafe_option)
         elif "noavx2" in dict and dict["noavx2"]:
-            runopts_var.set(runopts[4])
+            if noavx2_option is not None:
+                runopts_var.set(noavx2_option)
         elif "noblas" in dict and dict["noblas"]:
-            runopts_var.set(runopts[3])
+            if default_option is not None:
+                runopts_var.set(default_option)
+        elif openblas_option is not None:
+                runopts_var.set(openblas_option)
+        if "gpulayers" in dict and dict["gpulayers"]:
+            gpulayers_var.set(dict["gpulayers"])
         if "blasthreads" in dict and dict["blasthreads"]:
             blas_threads_var.set(str(dict["blasthreads"]))
         else:
             blas_threads_var.set("")
-
         if "contextsize" in dict and dict["contextsize"]:
             context_var.set(contextsize_text.index(str(dict["contextsize"])))
-
         if "ropeconfig" in dict and dict["ropeconfig"] and len(dict["ropeconfig"])>1:
             if dict["ropeconfig"][0]>0:
                 customrope_var.set(1)
@@ -1114,11 +1182,11 @@ def show_new_gui():
         if "forceversion" in dict and dict["forceversion"]:
             version_var.set(str(dict["forceversion"]))
 
-        if "mirostat" in dict and dict["mirostat"] and len(dict["mirostat"])>1:
-            usemirostat.set(0 if str(dict["mirostat"][0])=="0" else 1)
-            mirostat_var.set(str(dict["mirostat"][0]))
-            mirostat_tau.set(str(dict["mirostat"][1]))
-            mirostat_eta.set(str(dict["mirostat"][2]))
+        if "usemirostat" in dict and dict["usemirostat"] and len(dict["usemirostat"])>1:
+            usemirostat.set(0 if str(dict["usemirostat"][0])=="0" else 1)
+            mirostat_var.set(str(dict["usemirostat"][0]))
+            mirostat_tau.set(str(dict["usemirostat"][1]))
+            mirostat_eta.set(str(dict["usemirostat"][2]))
 
         if "model_param" in dict and dict["model_param"]:
             model_var.set(dict["model_param"])
@@ -1165,18 +1233,26 @@ def show_new_gui():
             import_vars(dict)
         pass
 
-    ctk.CTkButton(tabs , text = "Launch", fg_color="#2f8d3c", command = guilaunch, width=80, height = 35 ).grid(row=1,column=1, stick="se", padx= 25, pady=5)
+    def display_help():
+        try:
+            import webbrowser as wb
+            wb.open("https://github.com/LostRuins/koboldcpp/wiki")
+        except:
+            print("Cannot launch help browser.")
+
+    ctk.CTkButton(tabs , text = "Launch", fg_color="#2f8d3c", hover_color="#2faa3c", command = guilaunch, width=80, height = 35 ).grid(row=1,column=1, stick="se", padx= 25, pady=5)
 
-    ctk.CTkButton(tabs , text = "Save", fg_color="#084a66", command = save_config, width=60, height = 35 ).grid(row=1,column=1, stick="sw", padx= 5, pady=5)
-    ctk.CTkButton(tabs , text = "Load", fg_color="#084a66", command = load_config, width=60, height = 35 ).grid(row=1,column=1, stick="sw", padx= 70, pady=5)
+    ctk.CTkButton(tabs , text = "Save", fg_color="#084a66", hover_color="#085a88", command = save_config, width=60, height = 35 ).grid(row=1,column=1, stick="sw", padx= 5, pady=5)
+    ctk.CTkButton(tabs , text = "Load", fg_color="#084a66", hover_color="#085a88", command = load_config, width=60, height = 35 ).grid(row=1,column=1, stick="sw", padx= 70, pady=5)
+    ctk.CTkButton(tabs , text = "Help", fg_color="#992222", hover_color="#bb3333", command = display_help, width=60, height = 35 ).grid(row=1,column=1, stick="sw", padx= 135, pady=5)
 
-    ctk.CTkButton(tabs , text = "Old GUI", fg_color="#084a66", command = switch_old_gui, width=100, height = 35 ).grid(row=1,column=0, stick="sw", padx= 5, pady=5)
+    ctk.CTkButton(tabs , text = "Old GUI", fg_color="#084a66", hover_color="#085a88", command = switch_old_gui, width=100, height = 35 ).grid(row=1,column=0, stick="sw", padx= 5, pady=5)
     # runs main loop until closed or launch clicked
     root.mainloop()
 
     if nextstate==0:
         print("Exiting by user request.")
-        time.sleep(2)
+        time.sleep(3)
         sys.exit()
     elif nextstate==2:
         time.sleep(0.1)
@@ -1187,16 +1263,23 @@ def show_new_gui():
 
         if not args.model_param:
             print("\nNo ggml model file was selected. Exiting.")
-            time.sleep(2)
+            time.sleep(3)
             sys.exit(2)
 
-def show_gui_warning():
+def show_gui_warning(issue=None):
     from tkinter import messagebox
     import tkinter as tk
     root = tk.Tk()
     root.attributes("-alpha", 0)
-    messagebox.showerror(title="New GUI failed, using Old GUI", message="The new GUI failed to load.\n\nTo use new GUI, please install the customtkinter python module.")
-    root.destroy()
+    if issue == "No Backend Available":
+        messagebox.showerror(title="No Backends Available!", message="KoboldCPP couldn't locate any backends to use.\n\nTo use the program, please run the 'make' command from the directory.")
+        root.destroy()
+        print("No Backend Available (i.e Default, OpenBLAS, CLBlast, CuBLAS). To use the program, please run the 'make' command from the directory.")
+        time.sleep(3)
+        sys.exit(2)
+    else:
+        messagebox.showerror(title="New GUI failed, using Old GUI", message="The new GUI failed to load.\n\nTo use new GUI, please install the customtkinter python module.")
+        root.destroy()
 
 def show_old_gui():
     import tkinter as tk
@@ -1223,11 +1306,11 @@ def show_old_gui():
         tk.Label(root, text = "(Note: KoboldCpp only works with GGML model formats!)",
                 font = ("Arial", 9)).grid(row=1,column=0)
 
-        blasbatchopts = ["Don't Batch BLAS","BLAS = 32","BLAS = 64","BLAS = 128","BLAS = 256","BLAS = 512","BLAS = 1024"]
+        blasbatchopts = ["Don't Batch BLAS","BLAS = 32","BLAS = 64","BLAS = 128","BLAS = 256","BLAS = 512","BLAS = 1024","BLAS = 2048"]
         blaschoice = tk.StringVar()
         blaschoice.set("BLAS = 512")
 
-        runopts = ["Use OpenBLAS","Use CLBLast GPU #1","Use CLBLast GPU #2","Use CLBLast GPU #3","Use CuBLAS GPU","Use No BLAS","Use OpenBLAS (Old CPU, noavx2)","Failsafe Mode (Old CPU, noavx)"]
+        runopts = ["Use OpenBLAS","Use CLBLast GPU #1","Use CLBLast GPU #2","Use CLBLast GPU #3","Use CuBLAS GPU","Use No BLAS","NoAVX2 Mode (Old CPU)","Failsafe Mode (Old CPU)"]
         runchoice = tk.StringVar()
         runchoice.set("Use OpenBLAS")
 
@@ -1286,7 +1369,7 @@ def show_old_gui():
 
         if launchclicked==False:
             print("Exiting by user request.")
-            time.sleep(2)
+            time.sleep(3)
             sys.exit()
 
         #load all the vars
@@ -1318,7 +1401,6 @@ def show_old_gui():
             args.noavx2 = True
             args.noblas = True
             args.nommap = True
-            print("[Failsafe Mode : mmap is disabled.]")
 
         if selblaschoice==blasbatchopts[0]:
             args.blasbatchsize = -1
@@ -1334,6 +1416,8 @@ def show_old_gui():
             args.blasbatchsize = 512
         if selblaschoice==blasbatchopts[6]:
             args.blasbatchsize = 1024
+        if selblaschoice==blasbatchopts[7]:
+            args.blasbatchsize = 2048
 
         root = tk.Tk()
         root.attributes("-alpha", 0)
@@ -1341,7 +1425,7 @@ def show_old_gui():
         root.destroy()
         if not args.model_param:
             print("\nNo ggml model file was selected. Exiting.")
-            time.sleep(2)
+            time.sleep(3)
             sys.exit(2)
 
     else:
@@ -1351,7 +1435,7 @@ def show_old_gui():
         root.destroy()
         if not args.model_param:
             print("\nNo ggml model file was selected. Exiting.")
-            time.sleep(2)
+            time.sleep(3)
             sys.exit(2)
 
 #A very simple and stripped down embedded horde worker with no dependencies
@@ -1396,7 +1480,7 @@ def run_horde_worker(args, api_key, worker_name):
     BRIDGE_AGENT = f"KoboldCppEmbedWorker:1:https://github.com/LostRuins/koboldcpp"
     cluster = "https://horde.koboldai.net"
     while exitcounter < 10:
-        time.sleep(2)
+        time.sleep(3)
         readygo = make_url_request(f'{epurl}/api/v1/info/version', None,'GET')
         if readygo:
             print("Embedded Horde Worker is started.")
@@ -1472,10 +1556,10 @@ def run_horde_worker(args, api_key, worker_name):
         time.sleep(1)
     if exitcounter<100:
         print("Horde Worker Shutdown - Too many errors.")
-        time.sleep(2)
+        time.sleep(3)
     else:
         print("Horde Worker Shutdown - Server Closing.")
-        time.sleep(1)
+        time.sleep(2)
     sys.exit(2)
 
 def main(args):
@@ -1499,7 +1583,7 @@ def main(args):
                 except Exception as ex2:
                     print("File selection GUI unsupported. Please check command line: script.py --help")
                     print("Reason for no GUI: " + str(ex2))
-                    time.sleep(2)
+                    time.sleep(3)
                     sys.exit(2)
 
     if args.hordeconfig and args.hordeconfig[0]!="":
@@ -1543,20 +1627,20 @@ def main(args):
     time.sleep(1)
     if not os.path.exists(args.model_param):
         print(f"Cannot find model file: {args.model_param}")
-        time.sleep(2)
+        time.sleep(3)
         sys.exit(2)
 
     if args.lora and args.lora[0]!="":
         if not os.path.exists(args.lora[0]):
             print(f"Cannot find lora file: {args.lora[0]}")
-            time.sleep(2)
+            time.sleep(3)
             sys.exit(2)
         else:
             args.lora[0] = os.path.abspath(args.lora[0])
             if len(args.lora) > 1:
                 if not os.path.exists(args.lora[1]):
                     print(f"Cannot find lora base: {args.lora[1]}")
-                    time.sleep(2)
+                    time.sleep(3)
                     sys.exit(2)
                 else:
                     args.lora[1] = os.path.abspath(args.lora[1])
@@ -1577,7 +1661,7 @@ def main(args):
 
     if not loadok:
         print("Could not load model: " + modelname)
-        time.sleep(2)
+        time.sleep(3)
         sys.exit(3)
     try:
         basepath = os.path.abspath(os.path.dirname(__file__))
@@ -1605,6 +1689,7 @@ def main(args):
 
     if args.hordeconfig and len(args.hordeconfig)>4:
         horde_thread = threading.Thread(target=run_horde_worker,args=(args,args.hordeconfig[3],args.hordeconfig[4]))
+        horde_thread.daemon = True
         horde_thread.start()
 
     print(f"Please connect to custom endpoint at {epurl}")
@@ -1631,8 +1716,8 @@ if __name__ == '__main__':
     parser.add_argument("--blasthreads", help="Use a different number of threads during BLAS if specified. Otherwise, has the same value as --threads",metavar=('[threads]'), type=int, default=0)
     parser.add_argument("--psutil_set_threads", help="Experimental flag. If set, uses psutils to determine thread count based on physical cores.", action='store_true')
     parser.add_argument("--highpriority", help="Experimental flag. If set, increases the process CPU priority, potentially speeding up generation. Use caution.", action='store_true')
-    parser.add_argument("--contextsize", help="Controls the memory allocated for maximum context size, only change if you need more RAM for big contexts. (default 2048)", type=int,choices=[512,1024,2048,3072,4096,6144,8192], default=2048)
-    parser.add_argument("--blasbatchsize", help="Sets the batch size used in BLAS processing (default 512). Setting it to -1 disables BLAS mode, but keeps other benefits like GPU offload.", type=int,choices=[-1,32,64,128,256,512,1024], default=512)
+    parser.add_argument("--contextsize", help="Controls the memory allocated for maximum context size, only change if you need more RAM for big contexts. (default 2048)", type=int,choices=[512,1024,2048,3072,4096,6144,8192,12288,16384], default=2048)
+    parser.add_argument("--blasbatchsize", help="Sets the batch size used in BLAS processing (default 512). Setting it to -1 disables BLAS mode, but keeps other benefits like GPU offload.", type=int,choices=[-1,32,64,128,256,512,1024,2048], default=512)
     parser.add_argument("--ropeconfig", help="If set, uses customized RoPE scaling from configured frequency scale and frequency base (e.g. --ropeconfig 0.25 10000). Otherwise, uses NTK-Aware scaling set automatically based on context size. For linear rope, simply set the freq-scale and ignore the freq-base",metavar=('[rope-freq-scale]', '[rope-freq-base]'), default=[0.0, 10000.0], type=float, nargs='+')
     parser.add_argument("--stream", help="Uses streaming when generating tokens. Only for the Kobold Lite UI.", action='store_true')
     parser.add_argument("--smartcontext", help="Reserving a portion of context to try processing less frequently.", action='store_true')
@@ -1649,7 +1734,7 @@ if __name__ == '__main__':
     compatgroup = parser.add_mutually_exclusive_group()
     compatgroup.add_argument("--noblas", help="Do not use OpenBLAS for accelerated prompt ingestion", action='store_true')
     compatgroup.add_argument("--useclblast", help="Use CLBlast for GPU Acceleration. Must specify exactly 2 arguments, platform ID and device ID (e.g. --useclblast 1 0).", type=int, choices=range(0,9), nargs=2)
-    compatgroup.add_argument("--usecublas", help="Use CuBLAS for GPU Acceleration. Requires CUDA. Select lowvram to not allocate VRAM scratch buffer. Enter a number afterwards to select and use 1 GPU. Leaving no number will use all GPUs.", nargs='*',metavar=('[lowvram|normal] [main GPU ID]'), choices=['normal', 'lowvram', '0', '1', '2'])
+    compatgroup.add_argument("--usecublas", help="Use CuBLAS for GPU Acceleration. Requires CUDA. Select lowvram to not allocate VRAM scratch buffer. Enter a number afterwards to select and use 1 GPU. Leaving no number will use all GPUs.", nargs='*',metavar=('[lowvram|normal] [main GPU ID] [mmq]'), choices=['normal', 'lowvram', '0', '1', '2', 'mmq'])
     parser.add_argument("--gpulayers", help="Set number of layers to offload to GPU when using GPU. Requires GPU.",metavar=('[GPU layers]'), type=int, default=0)
     parser.add_argument("--tensor_split", help="For CUDA with ALL GPU set only, ratio to split tensors across multiple GPUs, space-separated list of proportions, e.g. 7 3", metavar=('[Ratios]'), type=float, nargs='+')
 
diff --git a/llama-util.h b/llama-util.h
index e1e7fd748c072ef38c347aa53b195e3a9a2bf8ea..a97520108c508de8dd5f8afa1f93cae5f9711b56 100644
--- a/llama-util.h
+++ b/llama-util.h
@@ -149,6 +149,46 @@ struct llama_file {
     }
 };
 
+// llama_context_data
+struct llama_data_context {
+    virtual void write(const void * src, size_t size) = 0;
+    virtual size_t get_size_written() = 0;
+    virtual ~llama_data_context() = default;
+};
+
+struct llama_data_buffer_context : llama_data_context {
+    uint8_t* ptr;
+    size_t size_written = 0;
+
+    llama_data_buffer_context(uint8_t * p) : ptr(p) {}
+
+    void write(const void * src, size_t size) override {
+        memcpy(ptr, src, size);
+        ptr += size;
+        size_written += size;
+    }
+
+    size_t get_size_written() override {
+        return size_written;
+    }
+};
+
+struct llama_data_file_context : llama_data_context {
+    llama_file* file;
+    size_t size_written = 0;
+
+    llama_data_file_context(llama_file * f) : file(f) {}
+
+    void write(const void * src, size_t size) override {
+        file->write_raw(src, size);
+        size_written += size;
+    }
+
+    size_t get_size_written() override {
+        return size_written;
+    }
+};
+
 #if defined(_WIN32)
 static std::string llama_format_win_err(DWORD err) {
     LPSTR buf;
@@ -179,7 +219,7 @@ struct llama_mmap {
         // prefetch/readahead impairs performance on NUMA systems
         if (numa) { prefetch = 0; }
 #ifdef __linux__
-        if (prefetch) { flags |= MAP_POPULATE; }
+        if (prefetch >= file->size) { flags |= MAP_POPULATE; }
 #endif
         addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
         if (addr == MAP_FAILED) {
@@ -247,7 +287,7 @@ struct llama_mmap {
         #pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
         #endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
         #else
-        printf("\nPrefetchVirtualMemory skipped in failsafe mode.");
+        printf("\nPrefetchVirtualMemory skipped in compatibility mode.\n");
         #endif
     }
 
diff --git a/llama.cpp b/llama.cpp
index 4818fe6c75774e9fd3cbdd719a7d913bc365c394..74c1f635ecc71f8dceedb9e60969f3762d407b0c 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -57,8 +57,14 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+#if !defined(GGML_USE_CUBLAS) && !defined(GGML_USE_METAL)
+#include "ggml-alloc.h"
+#define LLAMA_USE_ALLOCATOR
+#else
 #define LLAMA_USE_SCRATCH
 #define LLAMA_MAX_SCRATCH_BUFFERS 16
+#endif
+
 
 // available llama models
 enum e_model {
@@ -144,7 +150,7 @@ static const std::map<e_model, size_t> & MEM_REQ_EVAL()
 }
 
 // amount of VRAM needed per batch size to hold temporary results
-// the values for 3b and 65b are not derived from testing but instead chosen conservatively
+// the values for 3b are not derived from testing but instead chosen conservatively
 static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_BASE()
 {
     static std::map<e_model, size_t> k_sizes = {
@@ -152,14 +158,14 @@ static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_BASE()
         { MODEL_7B,   512ull * kB },
         { MODEL_13B,  640ull * kB },
         { MODEL_30B,  768ull * kB },
-        { MODEL_65B, 1536ull * kB },
-        { MODEL_70B, 1536ull * kB }, // TODO (likely can be reduced)
+        { MODEL_65B, 1360ull * kB },
+        { MODEL_70B, 1360ull * kB },
     };
     return k_sizes;
 }
 
 // amount of VRAM needed per batch size and context to hold temporary results
-// the values for 3b and 65b are not derived from testing but instead chosen conservatively
+// the values for 3b are not derived from testing but instead chosen conservatively
 static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
 {
     static std::map<e_model, size_t> k_sizes = {
@@ -167,8 +173,8 @@ static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
         { MODEL_7B,  128ull },
         { MODEL_13B, 160ull },
         { MODEL_30B, 208ull },
-        { MODEL_65B, 416ull },
-        { MODEL_70B, 416ull }, // TODO (likely can be reduced)
+        { MODEL_65B, 320ull },
+        { MODEL_70B, 320ull },
     };
     return k_sizes;
 }
@@ -328,13 +334,22 @@ struct llama_model {
 
 struct llama_context {
     llama_context(const llama_model & model) : model(model), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {}
-#ifdef GGML_USE_METAL
     ~llama_context() {
+        if (model_owner) {
+            delete &model;
+        }
+#ifdef GGML_USE_METAL
         if (ctx_metal) {
             ggml_metal_free(ctx_metal);
         }
-    }
 #endif
+#ifdef LLAMA_USE_ALLOCATOR
+        if (alloc) {
+            ggml_allocr_free(alloc);
+        }
+#endif
+    }
+
     std::mt19937 rng;
 
     bool has_evaluated_once = false;
@@ -372,7 +387,17 @@ struct llama_context {
     // memory buffers used to evaluate the model
     // TODO: move in llama_state
     llama_ctx_buffer buf_compute;
+
+#ifdef LLAMA_USE_ALLOCATOR
+    llama_ctx_buffer buf_alloc;
+    ggml_allocr * alloc = NULL;
+#endif
+
+#ifdef LLAMA_USE_SCRATCH
     llama_ctx_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
+    int    buf_last = 0;
+    size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
+#endif
 
 #ifdef GGML_USE_METAL
     ggml_metal_context * ctx_metal = NULL;
@@ -382,9 +407,6 @@ struct llama_context {
     ggml_mpi_context * ctx_mpi = NULL;
 #endif
 
-    int    buf_last = 0;
-    size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
-
     void use_buf(struct ggml_context * ctx, int i) {
 #if defined(LLAMA_USE_SCRATCH)
         size_t last_size = 0;
@@ -726,12 +748,12 @@ struct llama_model_loader {
 
     void load_all_data(llama_progress_callback progress_callback, void *  progress_callback_user_data, llama_mlock * lmlock) {
         size_t data_size = 0;
-        size_t prefetch_size = 0;
+        size_t prefetch_size = file_loader->file.size;
         size_t lock_size = 0;
         for (const llama_load_tensor & lt : tensors_map.tensors) {
             data_size += lt.size;
-            if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
-                prefetch_size += lt.size;
+            if (lt.ggml_tensor->backend != GGML_BACKEND_CPU) {
+                prefetch_size -= lt.size;
             }
         }
 
@@ -880,6 +902,7 @@ struct llama_context_params llama_context_default_params() {
         /*.progress_callback           =*/ nullptr,
         /*.progress_callback_user_data =*/ nullptr,
         /*.low_vram                    =*/ false,
+        /*.mul_mat_q                   =*/ false,
         /*.f16_kv                      =*/ true,
         /*.logits_all                  =*/ false,
         /*.vocab_only                  =*/ false,
@@ -914,6 +937,11 @@ bool llama_mlock_supported() {
     return llama_mlock::SUPPORTED;
 }
 
+int get_blas_batch_mul(int batch)
+{
+    return (batch>512?(batch>1024?4:2):1);
+}
+
 void llama_backend_init(bool numa) {
     ggml_time_init();
 
@@ -1007,6 +1035,7 @@ static void llama_model_load_internal(
         int n_gpu_layers,
         int main_gpu,
         const float * tensor_split,
+        const bool mul_mat_q,
         float rope_freq_base,
         float rope_freq_scale,
         bool low_vram,
@@ -1018,7 +1047,7 @@ static void llama_model_load_internal(
         void * progress_callback_user_data) {
 
     model.t_start_us = ggml_time_us();
-    size_t blasbatchmul = (n_batch>512?2:1);
+    size_t blasbatchmul = get_blas_batch_mul(n_batch);
 
     std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap));
 
@@ -1052,7 +1081,7 @@ static void llama_model_load_internal(
         // LLaMAv2
         // TODO: temporary until GGUF
         //patch for llama2 gqa
-        if (model.type == e_model::MODEL_65B && hparams.n_mult >= 4096) {
+        if (model.type == e_model::MODEL_65B && (hparams.n_mult >= 4096 && hparams.n_mult != 5504)) {
             fprintf(stderr, "%s: Applying KCPP Patch for 70B model, setting GQA to 8\n", __func__);
             n_gqa = 8;
         }
@@ -1141,9 +1170,11 @@ static void llama_model_load_internal(
     }
 
     (void) main_gpu;
+    (void) mul_mat_q;
 #if defined(GGML_USE_CUBLAS)
     fprintf(stderr, "%s: using CUDA for GPU acceleration\n", __func__);
     ggml_cuda_set_main_device(main_gpu);
+    ggml_cuda_set_mul_mat_q(mul_mat_q);
 #define LLAMA_BACKEND_OFFLOAD       GGML_BACKEND_GPU
 #define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
 #elif defined(GGML_USE_CLBLAST)
@@ -1237,12 +1268,16 @@ static void llama_model_load_internal(
         const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
 
         // this is the total memory required to run the inference
-        const size_t mem_required =
+        size_t mem_required =
             ctx_size +
-            mmapped_size - vram_weights + // weights in VRAM not in memory
+            mmapped_size - vram_weights; // weights in VRAM not in memory
+
+#ifndef LLAMA_USE_ALLOCATOR
+        mem_required +=
             blasbatchmul*MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) +
             blasbatchmul*MEM_REQ_SCRATCH1().at(model.type) +
             blasbatchmul*MEM_REQ_EVAL().at(model.type);
+#endif
 
         // this is the memory required by one llama_state
         const size_t mem_required_state =
@@ -1348,6 +1383,7 @@ static bool llama_model_load(
         int n_gpu_layers,
         int main_gpu,
         const float * tensor_split,
+        const bool mul_mat_q,
         float rope_freq_base,
         float rope_freq_scale,
         bool low_vram,
@@ -1358,7 +1394,8 @@ static bool llama_model_load(
         llama_progress_callback progress_callback,
         void *progress_callback_user_data) {
     try {
-        llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers, main_gpu, tensor_split, rope_freq_base, rope_freq_scale, low_vram, memory_type,
+        llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers,
+                                  main_gpu, tensor_split, mul_mat_q, rope_freq_base, rope_freq_scale, low_vram, memory_type,
                                   use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
         return true;
     } catch (const std::exception & err) {
@@ -1367,32 +1404,15 @@ static bool llama_model_load(
     }
 }
 
-// evaluate the transformer
-//
-//   - lctx:      llama context
-//   - tokens:    new batch of tokens to process
-//   - embd       embeddings input
-//   - n_tokens   number of tokens
-//   - n_past:    the context size so far
-//   - n_threads: number of threads to use
-//
-static bool llama_eval_internal(
+static struct ggml_cgraph * llama_build_graph(
          llama_context & lctx,
      const llama_token * tokens,
            const float * embd,
                    int   n_tokens,
-                   int   n_past,
-                   int   n_threads,
-            const char * cgraph_fname) {
+                   int   n_past) {
 
     LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
 
-#ifdef GGML_USE_MPI
-    ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
-#endif
-
-    const int64_t t_start_us = ggml_time_us();
-
     const int N = n_tokens;
 
     const auto & model   = lctx.model;
@@ -1408,10 +1428,8 @@ static bool llama_eval_internal(
     const int64_t n_head      = hparams.n_head;
     const int64_t n_head_kv   = hparams.n_head_kv;
     const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_vocab     = hparams.n_vocab;
     const int64_t n_embd_gqa  = hparams.n_embd_gqa();
 
-
     LLAMA_ASSERT(n_embd_head == hparams.n_rot);
 
     const float freq_base  = hparams.rope_freq_base;
@@ -1423,26 +1441,35 @@ static bool llama_eval_internal(
     auto & mem_per_token = lctx.mem_per_token;
     auto & buf_compute   = lctx.buf_compute;
 
+
     struct ggml_init_params params = {
         /*.mem_size   =*/ buf_compute.size,
         /*.mem_buffer =*/ buf_compute.addr,
         /*.no_alloc   =*/ false,
     };
 
-    struct ggml_context * ctx0 = ggml_init(params);
+#ifdef LLAMA_USE_ALLOCATOR
+    params.no_alloc = true;
+#endif
 
-    ggml_cgraph gf = {};
+    struct ggml_context * ctx0 = ggml_init(params);
 
-    // for big prompts, if BLAS is enabled, it is better to use only one thread
-    // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
-    n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
+    ggml_cgraph * gf = ggml_new_graph(ctx0);
 
     struct ggml_tensor * cur;
     struct ggml_tensor * inpL;
 
     if (tokens) {
         struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+
+#ifdef LLAMA_USE_ALLOCATOR
+        ggml_allocr_alloc(lctx.alloc, inp_tokens);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
+        }
+#else
         memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
+#endif
         ggml_set_name(inp_tokens, "inp_tokens");
 
         inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
@@ -1452,7 +1479,15 @@ static bool llama_eval_internal(
 #endif
 
         inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
+
+#ifdef LLAMA_USE_ALLOCATOR
+        ggml_allocr_alloc(lctx.alloc, inpL);
+        if (!ggml_allocr_is_measure(lctx.alloc)) {
+            memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
+        }
+#else
         memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
+#endif
     }
 
     const int i_gpu_start = n_layer - n_gpu_layers;
@@ -1479,6 +1514,17 @@ static bool llama_eval_internal(
     }
 #endif // GGML_USE_CUBLAS
 
+    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+#ifdef LLAMA_USE_ALLOCATOR
+    ggml_allocr_alloc(lctx.alloc, KQ_scale);
+    if (!ggml_allocr_is_measure(lctx.alloc)) {
+        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
+    }
+#else
+    ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
+#endif
+    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+
     for (int il = 0; il < n_layer; ++il) {
         ggml_format_name(inpL, "layer_inp_%d", il);
 
@@ -1548,8 +1594,8 @@ static bool llama_eval_internal(
                 ggml_set_name(v, "v");
 
                 // important: storing RoPE-ed version of K in the KV cache!
-                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
             }
 
             struct ggml_tensor * Q =
@@ -1574,9 +1620,6 @@ static bool llama_eval_internal(
             ggml_set_name(KQ, "KQ");
 
             // KQ_scaled = KQ / sqrt(n_embd_head)
-            struct ggml_tensor * KQ_scale = ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head));
-            ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
-
             // KQ_scaled shape [n_past + N, N, n_head, 1]
             struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
             offload_func_kq(KQ_scaled);
@@ -1692,9 +1735,6 @@ static bool llama_eval_internal(
 
     lctx.use_buf(ctx0, 0);
 
-    // used at the end to optionally extract the embeddings
-    struct ggml_tensor * embeddings = NULL;
-
     // norm
     {
         cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
@@ -1705,8 +1745,6 @@ static bool llama_eval_internal(
         cur = ggml_mul(ctx0, cur, model.norm);
         // offload_func_nr(cur); // TODO CPU + GPU mirrored backend
         ggml_set_name(cur, "result_norm");
-
-        embeddings = cur;
     }
 
     // lm_head
@@ -1718,23 +1756,103 @@ static bool llama_eval_internal(
     // logits -> probs
     //cur = ggml_soft_max_inplace(ctx0, cur);
 
-    // run the computation
-    ggml_build_forward_expand(&gf, cur);
+    ggml_build_forward_expand(gf, cur);
 
-    // fprintf(stderr, "graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf.n_nodes, gf.n_leafs);
+    if (mem_per_token == 0) {
+        mem_per_token = ggml_used_mem(ctx0)/N;
+    }
+
+#if 0
+    printf("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__,
+            ggml_used_mem(ctx0)/1024.0/1024.0,
+            lctx.get_buf_max_mem(0)/1024.0/1024.0,
+            lctx.get_buf_max_mem(1)/1024.0/1024.0,
+            lctx.work_buffer.size()/1024.0/1024.0,
+            n_past, N);
+#endif
+
+    ggml_free(ctx0);
+
+    return gf;
+}
+
+// evaluate the transformer
+//
+//   - lctx:      llama context
+//   - tokens:    new batch of tokens to process
+//   - embd       embeddings input
+//   - n_tokens   number of tokens
+//   - n_past:    the context size so far
+//   - n_threads: number of threads to use
+//
+static bool llama_eval_internal(
+         llama_context & lctx,
+     const llama_token * tokens,
+           const float * embd,
+                   int   n_tokens,
+                   int   n_past,
+                   int   n_threads,
+            const char * cgraph_fname) {
+
+    LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
+
+    const int64_t t_start_us = ggml_time_us();
+
+#ifdef GGML_USE_MPI
+    ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
+#endif
+
+    const int N = n_tokens;
+
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+
+    const auto & kv_self = lctx.kv_self;
+
+    LLAMA_ASSERT(!!kv_self.ctx);
+
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_vocab     = hparams.n_vocab;
+
+#ifdef LLAMA_USE_ALLOCATOR
+    ggml_allocr_reset(lctx.alloc);
+#endif
+
+    ggml_cgraph * gf = llama_build_graph(lctx, tokens, embd, n_tokens, n_past);
+
+#ifdef LLAMA_USE_ALLOCATOR
+    ggml_allocr_alloc_graph(lctx.alloc, gf);
+#endif
+
+    // fprintf(stderr, "graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+
+    // for big prompts, if BLAS is enabled, it is better to use only one thread
+    // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
+    n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
+
+    struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
+    struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
+
+    LLAMA_ASSERT(strcmp(res->name, "result_output") == 0);
+    LLAMA_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
 
 #if GGML_USE_MPI
-    ggml_mpi_graph_compute_pre(lctx.ctx_mpi, &gf, n_layer);
+    const int64_t n_layer = hparams.n_layer;
+    ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
 #endif
 
 #ifdef GGML_USE_METAL
     if (lctx.ctx_metal && N == 1) {
-        if (!ggml_metal_if_optimized(lctx.ctx_metal)) {
-            ggml_metal_graph_find_concurrency(lctx.ctx_metal,&gf);
-        }
+        // TODO: disabled until #2413 is resolved
+        //if (!ggml_metal_if_optimized(lctx.ctx_metal)) {
+        //    ggml_metal_graph_find_concurrency(lctx.ctx_metal, gf);
+        //}
         ggml_metal_set_n_cb     (lctx.ctx_metal, n_threads);
-        ggml_metal_graph_compute(lctx.ctx_metal, &gf);
-        ggml_metal_get_tensor   (lctx.ctx_metal, cur);
+        ggml_metal_graph_compute(lctx.ctx_metal, gf);
+        ggml_metal_get_tensor   (lctx.ctx_metal, res);
+        if (!lctx.embedding.empty()) {
+            ggml_metal_get_tensor(lctx.ctx_metal, embeddings);
+        }
     } else {
         // IMPORTANT:
         // Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
@@ -1752,34 +1870,32 @@ static bool llama_eval_internal(
             ggml_metal_get_tensor(lctx.ctx_metal, kv_self.v);
         }
 
-        ggml_graph_compute_helper(lctx.work_buffer, &gf, n_threads);
+        ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
     }
 #else
-    ggml_graph_compute_helper(lctx.work_buffer, &gf, n_threads);
+    ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
 #endif
 
 #if GGML_USE_MPI
-    ggml_mpi_graph_compute_post(lctx.ctx_mpi, &gf, n_layer);
+    ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
 #endif
 
     // update kv token count
     lctx.kv_self.n = n_past + N;
 
-    struct ggml_tensor * res = gf.nodes[gf.n_nodes - 1];
-
     if (cgraph_fname) {
-        ggml_graph_export(&gf, cgraph_fname);
+        ggml_graph_export(gf, cgraph_fname);
     }
 
 #ifdef GGML_PERF
     // print timing information per ggml operation (for debugging purposes)
     // requires GGML_PERF to be defined
-    ggml_graph_print(&gf);
+    ggml_graph_print(gf);
 #endif
 
     // plot the computation graph in dot format (for debugging purposes)
     //if (n_past%100 == 0) {
-    //    ggml_graph_dump_dot(&gf, NULL, "llama.dot");
+    //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
     //}
 
     // extract logits
@@ -1804,21 +1920,6 @@ static bool llama_eval_internal(
         memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(N - 1)), sizeof(float)*n_embd);
     }
 
-    if (mem_per_token == 0) {
-        mem_per_token = ggml_used_mem(ctx0)/N;
-    }
-
-#if 0
-    printf("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__,
-            ggml_used_mem(ctx0)/1024.0/1024.0,
-            lctx.get_buf_max_mem(0)/1024.0/1024.0,
-            lctx.get_buf_max_mem(1)/1024.0/1024.0,
-            lctx.work_buffer.size()/1024.0/1024.0,
-            n_past, N);
-#endif
-
-    ggml_free(ctx0);
-
     // measure the performance only for the single-token evals
     if (N == 1) {
         lctx.t_eval_us += ggml_time_us() - t_start_us;
@@ -1930,7 +2031,9 @@ struct llama_tokenizer {
             if (token == vocab_.token_to_id.end()) {
                 // output any symbols that did not form tokens as bytes.
                 for (int j = 0; j < (int) symbol.n; ++j) {
-                    llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
+                    // NOTE: old version, before #2420 - not sure what are the implications of this
+                    //llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
+                    llama_vocab::id token_id = vocab_.token_to_id.at(std::string(1, symbol.text[j]));
                     output.push_back(token_id);
                 }
             } else {
@@ -3107,7 +3210,7 @@ struct llama_model * llama_load_model_from_file(
     ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
 
     if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gqa, params.rms_norm_eps, params.n_gpu_layers,
-                params.main_gpu, params.tensor_split, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
+                params.main_gpu, params.tensor_split, params.mul_mat_q, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
                 memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
                 params.progress_callback_user_data)) {
         delete model;
@@ -3136,7 +3239,7 @@ struct llama_context * llama_new_context_with_model(
         params.seed = time(NULL);
     }
 
-    size_t blasbatchmul = (params.n_batch>512?2:1);
+    size_t blasbatchmul = get_blas_batch_mul(params.n_batch);
 
     unsigned cur_percentage = 0;
     if (params.progress_callback == NULL) {
@@ -3186,10 +3289,47 @@ struct llama_context * llama_new_context_with_model(
             ctx->embedding.resize(hparams.n_embd);
         }
 
-        ctx->buf_compute.resize(blasbatchmul*MEM_REQ_EVAL().at(ctx->model.type));
+#ifdef LLAMA_USE_ALLOCATOR
+        {
+            static const size_t tensor_alignment = 32;
+            // the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
+            ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
+
+            // create measure allocator
+            ctx->alloc = ggml_allocr_new_measure(tensor_alignment);
+
+            // build worst-case graph
+            int n_tokens = std::min((int)hparams.n_ctx, params.n_batch);
+            int n_past = hparams.n_ctx - n_tokens;
+            llama_token token = llama_token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
+            ggml_cgraph * gf = llama_build_graph(*ctx, &token, NULL, n_tokens, n_past);
+
+            // measure memory requirements for the graph
+            size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;
+
+            fprintf(stderr, "%s: compute buffer total size = %7.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
 
+            // debug - for comparison with scratch buffer
+            //size_t prev_req =
+            //    MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type) +
+            //    MEM_REQ_SCRATCH1().at(ctx->model.type) +
+            //    MEM_REQ_EVAL().at(ctx->model.type);
+            //fprintf(stderr, "%s: (debug) equivalent with scratch buffer = %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0);
+
+            // recreate allocator with exact memory requirements
+            ggml_allocr_free(ctx->alloc);
+
+            ctx->buf_alloc.resize(alloc_size);
+            ctx->alloc = ggml_allocr_new(ctx->buf_alloc.addr, ctx->buf_alloc.size, tensor_alignment);
+        }
+#else
+        ctx->buf_compute.resize(blasbatchmul*MEM_REQ_EVAL().at(ctx->model.type) + ggml_graph_overhead());
+#endif
+
+#ifdef LLAMA_USE_SCRATCH
         ctx->buf_scratch[0].resize(blasbatchmul*MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type));
         ctx->buf_scratch[1].resize(blasbatchmul*MEM_REQ_SCRATCH1().at(ctx->model.type));
+#endif
     }
 
 #ifdef GGML_USE_METAL
@@ -3259,9 +3399,6 @@ struct llama_context * llama_init_from_file(
 }
 
 void llama_free(struct llama_context * ctx) {
-    if (ctx->model_owner) {
-        delete &ctx->model;
-    }
     delete ctx;
 }
 
@@ -3620,10 +3757,20 @@ size_t llama_get_state_size(const struct llama_context * ctx) {
     return s_total;
 }
 
-// Copies the state to the specified destination address
-size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
-    uint8_t * out = dst;
-
+/** copy state data into either a buffer or file depending on the passed in context
+ *
+ * file context:
+ * llama_file file("/path", "wb");
+ * llama_data_file_context data_ctx(&file);
+ * llama_copy_state_data(ctx, &data_ctx);
+ *
+ * buffer context:
+ * std::vector<uint8_t> buf(max_size, 0);
+ * llama_data_buffer_context data_ctx(&buf.data());
+ * llama_copy_state_data(ctx, &data_ctx);
+ *
+*/
+void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
     // copy rng
     {
         std::stringstream rng_ss;
@@ -3635,8 +3782,8 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
         memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE);
         memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
 
-        memcpy(out, &rng_size,   sizeof(rng_size));    out += sizeof(rng_size);
-        memcpy(out, &rng_buf[0], LLAMA_MAX_RNG_STATE); out += LLAMA_MAX_RNG_STATE;
+        data_ctx->write(&rng_size,   sizeof(rng_size));
+        data_ctx->write(&rng_buf[0], LLAMA_MAX_RNG_STATE);
     }
 
     // copy logits
@@ -3644,25 +3791,29 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
         const size_t logits_cap  = ctx->logits.capacity();
         const size_t logits_size = ctx->logits.size();
 
-        memcpy(out, &logits_cap,  sizeof(logits_cap));  out += sizeof(logits_cap);
-        memcpy(out, &logits_size, sizeof(logits_size)); out += sizeof(logits_size);
+        data_ctx->write(&logits_cap,  sizeof(logits_cap));
+        data_ctx->write(&logits_size, sizeof(logits_size));
 
         if (logits_size) {
-            memcpy(out, ctx->logits.data(), logits_size * sizeof(float));
+            data_ctx->write(ctx->logits.data(), logits_size * sizeof(float));
         }
 
-        out += logits_cap * sizeof(float);
+        // If there is a gap between the size and the capacity, write padding
+        size_t padding_size = (logits_cap - logits_size) * sizeof(float);
+        if (padding_size > 0) {
+            std::vector<uint8_t> padding(padding_size, 0); // Create a buffer filled with zeros
+            data_ctx->write(padding.data(), padding_size);
+        }
     }
 
     // copy embeddings
     {
         const size_t embedding_size = ctx->embedding.size();
 
-        memcpy(out, &embedding_size, sizeof(embedding_size)); out += sizeof(embedding_size);
+        data_ctx->write(&embedding_size, sizeof(embedding_size));
 
         if (embedding_size) {
-            memcpy(out, ctx->embedding.data(), embedding_size * sizeof(float));
-            out += embedding_size * sizeof(float);
+            data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float));
         }
     }
 
@@ -3671,14 +3822,14 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
         const auto & kv_self = ctx->kv_self;
         const auto & hparams = ctx->model.hparams;
         const int    n_layer = hparams.n_layer;
-        const int    n_embd  = hparams.n_embd;
+        const int    n_embd  = hparams.n_embd_gqa();
         const int    n_ctx   = hparams.n_ctx;
 
         const size_t kv_size = kv_self.buf.size;
         const int    kv_ntok = llama_get_kv_cache_token_count(ctx);
 
-        memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size);
-        memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok);
+        data_ctx->write(&kv_size, sizeof(kv_size));
+        data_ctx->write(&kv_ntok, sizeof(kv_ntok));
 
         if (kv_size) {
             const size_t elt_size = ggml_element_size(kv_self.k);
@@ -3687,12 +3838,12 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
             ggml_cgraph gf{};
 
             ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
-            kout3d->data = out;
-            out += ggml_nbytes(kout3d);
+            std::vector<uint8_t> kout3d_data(ggml_nbytes(kout3d), 0);
+            kout3d->data = kout3d_data.data();
 
             ggml_tensor * vout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_ntok, n_embd, n_layer);
-            vout3d->data = out;
-            out += ggml_nbytes(vout3d);
+            std::vector<uint8_t> vout3d_data(ggml_nbytes(vout3d), 0);
+            vout3d->data = vout3d_data.data();
 
             ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
                 n_embd, kv_ntok, n_layer,
@@ -3707,15 +3858,20 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
             ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
 
             ggml_free(cpy_ctx);
+
+            // our data is now in the kout3d_data and vout3d_data buffers
+            // write them to file
+            data_ctx->write(kout3d_data.data(), kout3d_data.size());
+            data_ctx->write(vout3d_data.data(), vout3d_data.size());
         }
     }
+}
 
-    const size_t written  = out - dst;
-    const size_t max_size = llama_get_state_size(ctx);
-
-    LLAMA_ASSERT(written <= max_size);
+size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
+    llama_data_buffer_context data_ctx(dst);
+    llama_copy_state_data_internal(ctx, &data_ctx);
 
-    return written;
+    return data_ctx.get_size_written();
 }
 
 // Sets the state reading from the specified source address
@@ -3774,7 +3930,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
         const auto & kv_self = ctx->kv_self;
         const auto & hparams = ctx->model.hparams;
         const int    n_layer = hparams.n_layer;
-        const int    n_embd  = hparams.n_embd;
+        const int    n_embd  = hparams.n_embd_gqa();
         const int    n_ctx   = hparams.n_ctx;
 
         size_t kv_size;
@@ -3900,15 +4056,9 @@ bool llama_save_session_file(struct llama_context * ctx, const char * path_sessi
     file.write_u32((uint32_t) n_token_count);
     file.write_raw(tokens, sizeof(llama_token) * n_token_count);
 
-    // save the context state
-    {
-        const size_t n_state_size_max = llama_get_state_size(ctx);
-
-        std::vector<uint8_t> state_data(n_state_size_max);
-        const size_t n_state_size_cur = llama_copy_state_data(ctx, state_data.data());
-
-        file.write_raw(state_data.data(), n_state_size_cur);
-    }
+    // save the context state using stream saving
+    llama_data_file_context data_ctx(&file);
+    llama_copy_state_data_internal(ctx, &data_ctx);
 
     return true;
 }
diff --git a/llama.h b/llama.h
index df46f9b9cfa311ea78906e5d4eb1f7c8a651e055..fa1977f2d949241834f3936424174c2133ef3ac0 100644
--- a/llama.h
+++ b/llama.h
@@ -108,6 +108,7 @@ extern "C" {
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
         bool low_vram;   // if true, reduce VRAM usage at the cost of performance
+        bool mul_mat_q;  // if true, use experimental mul_mat_q kernels
         bool f16_kv;     // use fp16 for KV cache
         bool logits_all; // the llama_eval() call computes all logits, not just the last one
         bool vocab_only; // only load the vocabulary, no weights
diff --git a/make_old_pyinstaller.bat b/make_old_pyinstaller.bat
index e5d1113f8f4e56280ae56d6ef96eb5bb0c0e95aa..c72a2ce10fa4267cd49124b6f5333a53065bd29f 100644
--- a/make_old_pyinstaller.bat
+++ b/make_old_pyinstaller.bat
@@ -1,4 +1,4 @@
 echo This file is only for my own usage, please do not use it. I am lazy.
 
 set PATH=d:\\MainApplications\\KoboldAIGPT\\KoboldAI-Horde-Bridge\\python;d:\\MainApplications\\KoboldAIGPT\\KoboldAI-Horde-Bridge\\python\\Scripts;%PATH%
-PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./niko.ico" --add-data "./klite.embd;." --add-data "./koboldcpp.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_openblas_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./clblast.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp_nocuda.exe"
\ No newline at end of file
+PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./niko.ico" --add-data "./klite.embd;." --add-data "./koboldcpp.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./clblast.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp_nocuda.exe"
\ No newline at end of file
diff --git a/make_old_pyinstaller_cuda.bat b/make_old_pyinstaller_cuda.bat
index 004b88bbfe276cd5b193ae475e426199137b4006..dfe28457161498e37a7c13d8a399d15fda71cf54 100644
--- a/make_old_pyinstaller_cuda.bat
+++ b/make_old_pyinstaller_cuda.bat
@@ -1,4 +1,4 @@
 echo This file is only for my own usage, please do not use it. I am lazy.
 
 set PATH=d:\\MainApplications\\KoboldAIGPT\\KoboldAI-Horde-Bridge\\python;d:\\MainApplications\\KoboldAIGPT\\KoboldAI-Horde-Bridge\\python\\Scripts;%PATH%
-PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./nikogreen.ico" --add-data "./klite.embd;." --add-data "./koboldcpp.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_openblas_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cublas64_11.dll;." --add-data "./cublasLt64_11.dll;." --add-data "./cudart64_110.dll;." --add-data "./msvcp140.dll;." --add-data "./vcruntime140.dll;." --add-data "./vcruntime140_1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp.exe"
\ No newline at end of file
+PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./nikogreen.ico" --add-data "./klite.embd;." --add-data "./koboldcpp.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cublas64_11.dll;." --add-data "./cublasLt64_11.dll;." --add-data "./cudart64_110.dll;." --add-data "./msvcp140.dll;." --add-data "./vcruntime140.dll;." --add-data "./vcruntime140_1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp.exe"
\ No newline at end of file
diff --git a/make_pyinstaller.bat b/make_pyinstaller.bat
index 12be110f88258034659d17571e16e9254caba115..3cf867b28c3feed10f1731c1ba6b5cda2a64284e 100644
--- a/make_pyinstaller.bat
+++ b/make_pyinstaller.bat
@@ -1 +1 @@
-PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./niko.ico" --add-data "./klite.embd;." --add-data "./koboldcpp.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_openblas_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./clblast.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp.exe"
\ No newline at end of file
+PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./niko.ico" --add-data "./klite.embd;." --add-data "./koboldcpp.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./clblast.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp.exe"
\ No newline at end of file
diff --git a/make_pyinstaller.sh b/make_pyinstaller.sh
index 155a1cd82bcdad51173966048dfcfede15153883..270850663ee79a3f061823a4a43214fbae40b6d2 100644
--- a/make_pyinstaller.sh
+++ b/make_pyinstaller.sh
@@ -5,7 +5,7 @@ pyinstaller --noconfirm --onefile --clean --console --collect-all customtkinter
 --add-data "./koboldcpp.so:." \
 --add-data "./koboldcpp_openblas.so:." \
 --add-data "./koboldcpp_failsafe.so:." \
---add-data "./koboldcpp_openblas_noavx2.so:." \
+--add-data "./koboldcpp_noavx2.so:." \
 --add-data "./koboldcpp_clblast.so:." \
 --add-data "./rwkv_vocab.embd:." \
 --add-data "./rwkv_world_vocab.embd:." \
diff --git a/make_pyinstaller_hybrid_henk.bat b/make_pyinstaller_hybrid_henk.bat
index be25d2ebf6debf285626181da7d022db60b1d2cb..a99b1569dcdd7f952b8f3dc20fe1f19379e3602e 100644
--- a/make_pyinstaller_hybrid_henk.bat
+++ b/make_pyinstaller_hybrid_henk.bat
@@ -2,4 +2,4 @@ cd /d "%~dp0"
 copy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\bin\cudart64_110.dll" .\ /Y
 copy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\bin\cublasLt64_11.dll" .\ /Y
 copy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\bin\cublas64_11.dll" .\ /Y
-PyInstaller --noconfirm --onefile --collect-all customtkinter --clean --console --icon ".\niko.ico" --add-data "./klite.embd;." --add-data "./koboldcpp.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_openblas_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cudart64_110.dll;." --add-data "./cublasLt64_11.dll;." --add-data "./cublas64_11.dll;." --add-data "./rwkv_vocab.embd;." --add-data "C:/Windows/System32/msvcp140.dll;." --add-data "C:/Windows/System32/vcruntime140_1.dll;." "./koboldcpp.py" -n "koboldcpp.exe"
\ No newline at end of file
+PyInstaller --noconfirm --onefile --collect-all customtkinter --clean --console --icon ".\niko.ico" --add-data "./klite.embd;." --add-data "./koboldcpp.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cudart64_110.dll;." --add-data "./cublasLt64_11.dll;." --add-data "./cublas64_11.dll;." --add-data "./rwkv_vocab.embd;." --add-data "C:/Windows/System32/msvcp140.dll;." --add-data "C:/Windows/System32/vcruntime140_1.dll;." "./koboldcpp.py" -n "koboldcpp.exe"
\ No newline at end of file
diff --git a/model_adapter.cpp b/model_adapter.cpp
index da9fa193edc4bcdc3e9c31506bb512c8458b1038..514764ec0f52492ae5b7b72bc53bd97c0f98d71b 100644
--- a/model_adapter.cpp
+++ b/model_adapter.cpp
@@ -133,28 +133,36 @@ void print_tok_vec(std::vector<float> &embd)
        else if(vocabsiz==50257 || (vocabsiz>=49152&&vocabsiz<=49157)) //49152-6 is starcoder
        {
            fileformat = FileFormat::GPT2_1;
-           uint32_t temp;
-           fin.read((char *)&temp, sizeof(temp)); //ctx
-           fin.read((char *)&temp, sizeof(temp)); //n_embd
-           fin.read((char *)&temp, sizeof(temp)); //n_head
+           uint32_t temp, v1,v2,v3;
+           fin.read((char *)&v1, sizeof(temp)); //ctx
+           fin.read((char *)&v2, sizeof(temp)); //n_embd
+           fin.read((char *)&v3, sizeof(temp)); //n_head
            fin.read((char *)&temp, sizeof(temp)); //n_layer
-           fin.read((char *)&temp, sizeof(temp)); //f16
-           const int32_t qntvr = temp / 1000;
-           temp %= 1000;
-           if (qntvr != 0)
+           if(vocabsiz==49152 && v1==4096 && v2==2560 && v3==32 && temp==32)
            {
-               if (qntvr == 1)
-               {
-                   fileformat = FileFormat::GPT2_3;
-               }
-               else
-               {
-                   fileformat = FileFormat::GPT2_4;
-               }
+                //special case, Stablecode Completion Alpha 3B
+               fileformat = FileFormat::NEOX_6;
            }
-           else if (temp != 0 && temp != 1)
+           else
            {
-               fileformat = FileFormat::GPT2_2; //quantized format cannot be legacy type
+                fin.read((char *)&temp, sizeof(temp)); //f16
+                const int32_t qntvr = temp / 1000;
+                temp %= 1000;
+                if (qntvr != 0)
+                {
+                    if (qntvr == 1)
+                    {
+                        fileformat = FileFormat::GPT2_3;
+                    }
+                    else
+                    {
+                        fileformat = FileFormat::GPT2_4;
+                    }
+                }
+                else if (temp != 0 && temp != 1)
+                {
+                    fileformat = FileFormat::GPT2_2; //quantized format cannot be legacy type
+                }
            }
        }
        else if(vocabsiz < 31998 || vocabsiz > 33000)
diff --git a/otherarch/ggml_v1.c b/otherarch/ggml_v1.c
index d6af52c820fde1e890798fe2240794d3a8bdb577..1c96d6dceb394b30383e8218231dc79c29c4060e 100644
--- a/otherarch/ggml_v1.c
+++ b/otherarch/ggml_v1.c
@@ -154,9 +154,11 @@ typedef double ggml_v1_float;
 #undef bool
 #define bool _Bool
 #else
+#if !defined(__riscv)
 #include <immintrin.h>
 #endif
 #endif
+#endif
 
 #ifdef __F16C__
 
@@ -1577,63 +1579,6 @@ inline static void ggml_v1_vec_mad_q4_0(const int n, float * restrict y, void *
     const float   * restrict pd = (const float *)   (x);
     const uint8_t * restrict pb = (const uint8_t *) (pd + nb);
 
-#if __ARM_NEON
-#if QK == 32
-    for (int i = 0; i < nb; ++i) {
-        const float d0 = pd[i]*v;
-
-        const uint8_t * restrict pp = pb + i*16;
-
-        const uint8x8_t m4b = vdup_n_u8(0xf);
-        const int8x8_t  s8b = vdup_n_s8(0x8);
-
-        const float32x4_t vd = vdupq_n_f32(d0);
-
-        for (int j = 0; j < 2; j++) {
-            const uint8x8_t vx = vld1_u8(pp + j*8);
-
-            const int8x8_t vxl = vreinterpret_s8_u8(vand_u8(vx, m4b));
-            const int8x8_t vxh = vreinterpret_s8_u8(vshr_n_u8(vx, 4));
-
-            // sub 8
-            const int8x8_t vxls = vsub_s8(vxl, s8b);
-            const int8x8_t vxhs = vsub_s8(vxh, s8b);
-
-            //const int8x8_t vxlt = vzip_s8(vxls, vxhs)[0];
-            //const int8x8_t vxht = vzip_s8(vxls, vxhs)[1];
-            const int8x8_t vxlt = vzip1_s8(vxls, vxhs);
-            const int8x8_t vxht = vzip2_s8(vxls, vxhs);
-
-            const int8x16_t vxq = vcombine_s8(vxlt, vxht);
-
-            // convert to 2x int16x8_t
-            const int16x8_t vxq0 = vmovl_s8(vget_low_s8 (vxq));
-            const int16x8_t vxq1 = vmovl_s8(vget_high_s8(vxq));
-
-            // convert to 4x float32x4_t
-            const float32x4_t vx0 = vcvtq_f32_s32(vmovl_s16(vget_low_s16 (vxq0)));
-            const float32x4_t vx1 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vxq0)));
-            const float32x4_t vx2 = vcvtq_f32_s32(vmovl_s16(vget_low_s16 (vxq1)));
-            const float32x4_t vx3 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vxq1)));
-
-            const float32x4_t vy0 = vld1q_f32(y + i*32 + j*16 + 0);
-            const float32x4_t vy1 = vld1q_f32(y + i*32 + j*16 + 4);
-            const float32x4_t vy2 = vld1q_f32(y + i*32 + j*16 + 8);
-            const float32x4_t vy3 = vld1q_f32(y + i*32 + j*16 + 12);
-
-            const float32x4_t vr0 = vfmaq_f32(vy0, vx0, vd);
-            const float32x4_t vr1 = vfmaq_f32(vy1, vx1, vd);
-            const float32x4_t vr2 = vfmaq_f32(vy2, vx2, vd);
-            const float32x4_t vr3 = vfmaq_f32(vy3, vx3, vd);
-
-            vst1q_f32(y + i*32 + j*16 + 0,  vr0);
-            vst1q_f32(y + i*32 + j*16 + 4,  vr1);
-            vst1q_f32(y + i*32 + j*16 + 8,  vr2);
-            vst1q_f32(y + i*32 + j*16 + 12, vr3);
-        }
-    }
-#endif
-#else
     // scalar
     for (int i = 0; i < nb; i++) {
         const float d = pd[i];
@@ -1658,7 +1603,6 @@ inline static void ggml_v1_vec_mad_q4_0(const int n, float * restrict y, void *
             assert(!isinf(y[i*QK + l + 1]));
         }
     }
-#endif
 }
 
 inline static void ggml_v1_vec_mad_q4_1(const int n, float * restrict y, void * restrict x, const float v) {
@@ -10056,7 +10000,7 @@ enum ggml_v1_opt_result ggml_v1_opt(
         struct ggml_v1_init_params params_ctx;
         params_ctx.mem_size   = 16*1024*1024;
         params_ctx.mem_buffer = NULL;
-       
+
 
         ctx = ggml_v1_init(params_ctx);
         if (ctx == NULL) {
diff --git a/otherarch/gpt2_v3.cpp b/otherarch/gpt2_v3.cpp
index 608a61ac226d7a5e550d122ecdfa52bbd4e50173..dd178ca021760390ad1c4a24307f202d35afdbc5 100644
--- a/otherarch/gpt2_v3.cpp
+++ b/otherarch/gpt2_v3.cpp
@@ -416,9 +416,10 @@ bool gpt2_eval(
 
     // use 2 scratch buffers
     // TODO: very hacky solution - reimplement in a more elegant way
-    static size_t scr0_size = (n_embd>2400?512u:256u)*1024*1024;
+    static size_t scr0_size = (n_embd>2400?512u:256u)*1024*1024*(hparams.n_ctx>8192?2:1);
     static size_t scr1_size = (n_embd>2400?512u:256u)*1024*1024;
 
+
     static void * scr0 = malloc(scr0_size);
     static void * scr1 = malloc(scr1_size);
 
diff --git a/otherarch/gptj_v3.cpp b/otherarch/gptj_v3.cpp
index 91eb355bb58400fb62a7e9fc17db8e9df531fe10..1cc566208f7809f31463a6104ed5370c1fe136bd 100644
--- a/otherarch/gptj_v3.cpp
+++ b/otherarch/gptj_v3.cpp
@@ -417,7 +417,7 @@ bool gptj_eval(
 
     // use 2 scratch buffers
     // TODO: very hacky solution - reimplement in a more elegant way
-    static size_t scr0_size = 512u*1024*1024;
+    static size_t scr0_size = 512u*1024*1024*(hparams.n_ctx>8192?2:1);
     static size_t scr1_size = 512u*1024*1024;
 
     static void * scr0 = malloc(scr0_size);
diff --git a/otherarch/llama_v2-util.h b/otherarch/llama_v2-util.h
index 00aedf8e64ecd495440029bacf7779f68ee53921..63942a007301b5d17d063f9a1e5b59c9b1a0ffd7 100644
--- a/otherarch/llama_v2-util.h
+++ b/otherarch/llama_v2-util.h
@@ -225,7 +225,7 @@ struct llama_v2_mmap {
         #pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
         #endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
         #else
-        printf("\nPrefetchVirtualMemory skipped in failsafe mode.");
+        printf("\nPrefetchVirtualMemory skipped in compatibility mode.\n");
         #endif
     }
 
diff --git a/otherarch/llama_v2.cpp b/otherarch/llama_v2.cpp
index 9ba3b236b633825be58efbe0b2c5a140c7ff71cc..ce7e2f771554d9438507622f6be9066099992dc6 100644
--- a/otherarch/llama_v2.cpp
+++ b/otherarch/llama_v2.cpp
@@ -38,7 +38,7 @@
 #include <sstream>
 #include <numeric>
 
-#define LLAMA_USE_SCRATCH
+#define LLAMA_V2_USE_SCRATCH
 #define LLAMA_V2_MAX_SCRATCH_BUFFERS 16
 
 // available llama models
@@ -247,7 +247,7 @@ struct llama_v2_context {
     size_t buf_max_size[LLAMA_V2_MAX_SCRATCH_BUFFERS] = { 0 };
 
     void use_buf(struct ggml_v2_context * ctx, int i) {
-#if defined(LLAMA_USE_SCRATCH)
+#if defined(LLAMA_V2_USE_SCRATCH)
         size_t last_size = 0;
 
         if (i == -1) {
@@ -269,7 +269,7 @@ struct llama_v2_context {
     }
 
     size_t get_buf_max_mem(int i) const {
-#if defined(LLAMA_USE_SCRATCH)
+#if defined(LLAMA_V2_USE_SCRATCH)
         return buf_max_size[i];
 #else
         (void) i;
diff --git a/otherarch/mpt_v3.cpp b/otherarch/mpt_v3.cpp
index 211464f89c4d88017ec20d4301aad637356ac4d4..6d2b46568176ae76d4df0e2a26c88a904e077f5e 100644
--- a/otherarch/mpt_v3.cpp
+++ b/otherarch/mpt_v3.cpp
@@ -354,7 +354,7 @@ bool mpt_eval(const mpt_model & model, const int n_threads, const int n_past,
     // use 2 scratch buffers
     // TODO: very hacky solution - reimplement in a more elegant way
     //MPT 30B needs more scratch memory
-    static size_t scr0_size = (n_embd>=7168?2048u:1024u)*1024*1024;
+    static size_t scr0_size = (n_embd>=7168?2048u:1024u)*1024*1024*(hparams.n_ctx>8192?2:1);
     static size_t scr1_size = (n_embd>=7168?2048u:1024u)*1024*1024;
 
     static void * scr0 = malloc(scr0_size);
diff --git a/otherarch/neox_v3.cpp b/otherarch/neox_v3.cpp
index fdcaed9bba8fb4a0ef25fecb651e9511e1ee5511..61bc9230e90eb40f11aa3cb5e1c195b78b9447d2 100644
--- a/otherarch/neox_v3.cpp
+++ b/otherarch/neox_v3.cpp
@@ -433,7 +433,7 @@ bool gpt_neox_eval(
 
     // use 2 scratch buffers
     // TODO: very hacky solution - reimplement in a more elegant way
-    static size_t scr0_size = (n_embd>2400?512u:256u)*1024*1024;
+    static size_t scr0_size = (n_embd>2400?512u:256u)*1024*1024*(hparams.n_ctx>8192?2:1);
     static size_t scr1_size = (n_embd>2400?512u:256u)*1024*1024;
 
     static void * scr0 = malloc(scr0_size);
diff --git a/spm-headers/ggml.h b/spm-headers/ggml.h
index 0d4a4b297f33ed19a0735e3ab870324f36d5195a..bdbd12800433242dec1e6dac8c96875540dd19e7 100644
--- a/spm-headers/ggml.h
+++ b/spm-headers/ggml.h
@@ -183,6 +183,15 @@
 #    define GGML_API
 #endif
 
+// TODO: support for clang
+#ifdef __GNUC__
+#    define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
+#elif defined(_MSC_VER)
+#    define GGML_DEPRECATED(func, hint) __declspec(deprecated(hint)) func
+#else
+#    define GGML_DEPRECATED(func, hint) func
+#endif
+
 #include <stdint.h>
 #include <stddef.h>
 #include <stdbool.h>
@@ -208,6 +217,8 @@
 
 #define GGML_UNUSED(x) (void)(x)
 
+#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
+
 #define GGML_ASSERT(x) \
     do { \
         if (!(x)) { \
@@ -372,6 +383,10 @@ extern "C" {
         GGML_OP_MAP_UNARY,
         GGML_OP_MAP_BINARY,
 
+        GGML_OP_MAP_CUSTOM1_F32,
+        GGML_OP_MAP_CUSTOM2_F32,
+        GGML_OP_MAP_CUSTOM3_F32,
+
         GGML_OP_MAP_CUSTOM1,
         GGML_OP_MAP_CUSTOM2,
         GGML_OP_MAP_CUSTOM3,
@@ -395,6 +410,12 @@ extern "C" {
         GGML_UNARY_OP_SILU,
     };
 
+    enum ggml_object_type {
+        GGML_OBJECT_TENSOR,
+        GGML_OBJECT_GRAPH,
+        GGML_OBJECT_WORK_BUFFER
+    };
+
     // ggml object
     struct ggml_object {
         size_t offs;
@@ -402,7 +423,9 @@ extern "C" {
 
         struct ggml_object * next;
 
-        char padding[8];
+        enum ggml_object_type type;
+
+        char padding[4];
     };
 
     static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
@@ -423,7 +446,7 @@ extern "C" {
         enum ggml_op op;
 
         // op params - allocated as int32_t for alignment
-        int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(uint32_t)];
+        int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
 
         bool is_param;
 
@@ -484,6 +507,8 @@ extern "C" {
         int64_t perf_time_us;
     };
 
+    static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph);
+
     // scratch buffer
     struct ggml_scratch {
         size_t offs;
@@ -558,6 +583,8 @@ extern "C" {
     GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
     GGML_API bool ggml_is_permuted  (const struct ggml_tensor * tensor);
 
+    GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
+
     // use this to compute the memory overhead of a tensor
     GGML_API size_t ggml_tensor_overhead(void);
 
@@ -1158,7 +1185,18 @@ extern "C" {
             int                   mode,
             int                   n_ctx);
 
-    // custom RoPE, in-place, returns view(a)
+    // custom RoPE
+    GGML_API struct ggml_tensor * ggml_rope_custom(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx,
+            float                 freq_base,
+            float                 freq_scale);
+
+    // in-place, returns view(a)
     GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1217,7 +1255,7 @@ extern "C" {
 
     // conv_1d with padding = half
     // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
-    GGML_API struct ggml_tensor* ggml_conv_1d_ph(
+    GGML_API struct ggml_tensor * ggml_conv_1d_ph(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             struct ggml_tensor  * b,
@@ -1230,7 +1268,7 @@ extern "C" {
         GGML_OP_POOL_COUNT,
     };
 
-    GGML_API struct ggml_tensor* ggml_pool_1d(
+    GGML_API struct ggml_tensor * ggml_pool_1d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             enum ggml_op_pool     op,
@@ -1238,7 +1276,7 @@ extern "C" {
             int                   s0, // stride
             int                   p0); // padding
 
-    GGML_API struct ggml_tensor* ggml_pool_2d(
+    GGML_API struct ggml_tensor * ggml_pool_2d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             enum ggml_op_pool     op,
@@ -1292,15 +1330,6 @@ extern "C" {
             int                   h0,
             int                   w);
 
-    // custom operators
-
-    typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
-    typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
-
-    typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
-    typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
-    typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
-
     GGML_API struct ggml_tensor * ggml_unary(
             struct ggml_context * ctx,
              struct ggml_tensor * a,
@@ -1311,63 +1340,138 @@ extern "C" {
         struct ggml_tensor  * a,
         enum ggml_unary_op op);
 
-    GGML_API struct ggml_tensor * ggml_map_unary_f32(
+    // custom operators
+
+    typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
+    typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
+
+    typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
+    typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
+    typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32(
             struct ggml_context        * ctx,
             struct ggml_tensor         * a,
-                   ggml_unary_op_f32_t   fun);
+                   ggml_unary_op_f32_t   fun),
+        "use ggml_map_custom1 instead");
 
-    GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
             struct ggml_context        * ctx,
             struct ggml_tensor         * a,
-                   ggml_unary_op_f32_t   fun);
+                   ggml_unary_op_f32_t   fun),
+        "use ggml_map_custom1_inplace instead");
 
-    GGML_API struct ggml_tensor * ggml_map_binary_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32(
             struct ggml_context         * ctx,
             struct ggml_tensor          * a,
             struct ggml_tensor          * b,
-                   ggml_binary_op_f32_t   fun);
+                   ggml_binary_op_f32_t   fun),
+        "use ggml_map_custom2 instead");
 
-    GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
             struct ggml_context         * ctx,
             struct ggml_tensor          * a,
             struct ggml_tensor          * b,
-                   ggml_binary_op_f32_t   fun);
+                   ggml_binary_op_f32_t   fun),
+        "use ggml_map_custom2_inplace instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom1_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
-                   ggml_custom1_op_f32_t   fun);
+                   ggml_custom1_op_f32_t   fun),
+        "use ggml_map_custom1 instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
-                   ggml_custom1_op_f32_t   fun);
+                   ggml_custom1_op_f32_t   fun),
+        "use ggml_map_custom1_inplace instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom2_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
             struct ggml_tensor           * b,
-                   ggml_custom2_op_f32_t   fun);
+                   ggml_custom2_op_f32_t   fun),
+        "use ggml_map_custom2 instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
             struct ggml_tensor           * b,
-                   ggml_custom2_op_f32_t   fun);
+                   ggml_custom2_op_f32_t   fun),
+        "use ggml_map_custom2_inplace instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom3_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
             struct ggml_tensor           * b,
             struct ggml_tensor           * c,
-                   ggml_custom3_op_f32_t   fun);
+                   ggml_custom3_op_f32_t   fun),
+        "use ggml_map_custom3 instead");
 
-    GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
             struct ggml_context          * ctx,
             struct ggml_tensor           * a,
             struct ggml_tensor           * b,
             struct ggml_tensor           * c,
-                   ggml_custom3_op_f32_t   fun);
+                   ggml_custom3_op_f32_t   fun),
+        "use ggml_map_custom3_inplace instead");
+
+    // custom operators v2
+
+    typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
+    typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
+    typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
+
+    #define GGML_N_TASKS_MAX -1
+
+    GGML_API struct ggml_tensor * ggml_map_custom1(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            ggml_custom1_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom1_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            ggml_custom1_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom2(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            ggml_custom2_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom2_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            ggml_custom2_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom3(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            struct ggml_tensor    * c,
+            ggml_custom3_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom3_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            struct ggml_tensor    * c,
+            ggml_custom3_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
 
     // loss function
 
@@ -1390,11 +1494,17 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * tensor);
 
+
     GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
 
     GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
     GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
 
+    // graph allocation in a context
+    GGML_API struct ggml_cgraph * ggml_new_graph        (struct ggml_context * ctx);
+    GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor);
+    GGML_API size_t ggml_graph_overhead(void);
+
     // ggml_graph_plan() has to be called before ggml_graph_compute()
     // when plan.work_size > 0, caller must allocate memory for plan.work_data
     GGML_API struct ggml_cplan ggml_graph_plan   (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);