initial commit

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+imatrix-*.dat filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,553 @@
 ---
+quantized_by: ubergarm
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
 license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
+base_model_relation: quantized
+tags:
+- imatrix
+- conversational
+- qwen3_moe
+- ik_llama.cpp
 ---
+
+## `ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-Coder-30B-A3B-Instruct
+This quant collection **REQUIRES** [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) fork to support the ik's latest SOTA quants and optimizations! Do **not** download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!
+
+*NOTE* `ik_llama.cpp` can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants.
+
+Some of ik's new quants are supported with [Nexesenex/croco.cpp](https://github.com/Nexesenex/croco.cpp) fork of KoboldCPP.
+
+These quants provide best in class perplexity for the given memory footprint.
+
+## Big Thanks
+Shout out to Wendell and the **Level1Techs** crew, the community [Forums](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)!  **BIG thanks** for providing **BIG hardware** expertise and access to run these experiments and make these great quants available to the community!!!
+
+Also thanks to all the folks in the quanting and inferencing community on [BeaverAI Club Discord](https://huggingface.co/BeaverAI) and on [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) for tips and tricks helping each other run, test, and benchmark all the fun new models!
+
+## Quant Collection
+Perplexity computed against *wiki.test.raw*.
+
+![Perplexity Chart](images/perplexity.png "Chart showing Perplexity improving as BPW increases.")
+
+These first three are just test quants for baseline perplexity comparison:
+* `bf16` 56.894 GiB (16.007 BPW)
+  - Final estimate: PPL = TODO
+* `Q8_0` 30.247 GiB (8.510 BPW)
+  - Final estimate: PPL = TODO
+* `Q4_0` 16.111 GiB (4.533 BPW)
+  - Final estimate: PPL = TODO
+
+## `IQ5_K` 21.324 GiB (5.999 BPW)
+Final estimate: PPL = TODO
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 48 Repeating Layers [0-47]
+
+# Attention
+blk\.(0)\.attn_q.*=q8_0
+blk\.(0)\.attn_k.*=q8_0
+blk\.(0)\.attn_v.*=q8_0
+blk\.(0)\.attn_output.*=q8_0
+
+blk\..*\.attn_q.*=iq5_k
+blk\..*\.attn_k.*=iq6_k
+blk\..*\.attn_v.*=iq6_k
+blk\..*\.attn_output.*=iq5_k
+
+# Routed Experts
+blk\.(0|47)\.ffn_down_exps\.weight=q8_0
+blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0
+
+blk\..*\.ffn_down_exps\.weight=iq6_k
+blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k
+
+# Non-Repeating Layers
+token_embd\.weight=iq6_k
+output\.weight=iq6_k
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ5_K.gguf \
+    IQ5_K \
+    192
+```
+
+</details>
+
+## `IQ4_K` 17.878 GiB (5.030 BPW)
+Final estimate: PPL = TODO
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 48 Repeating Layers [0-47]
+
+# Attention
+blk\.(0)\.attn_q.*=q8_0
+blk\.(0)\.attn_k.*=q8_0
+blk\.(0)\.attn_v.*=q8_0
+blk\.(0)\.attn_output.*=q8_0
+
+blk\..*\.attn_q.*=iq5_k
+blk\..*\.attn_k.*=iq6_k
+blk\..*\.attn_v.*=iq6_k
+blk\..*\.attn_output.*=iq5_k
+
+# Routed Experts
+blk\.(0|47)\.ffn_down_exps\.weight=q8_0
+blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0
+
+blk\..*\.ffn_down_exps\.weight=iq5_k
+blk\..*\.ffn_(gate|up)_exps\.weight=iq4_k
+
+# Non-Repeating Layers
+token_embd\.weight=iq4_k
+output\.weight=iq6_k
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ4_K.gguf \
+    IQ4_K \
+    192
+```
+
+</details>
+
+## `IQ4_KSS` 15.531 GiB (4.370 BPW)
+Final estimate: PPL = TODO
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 48 Repeating Layers [0-47]
+
+# Attention
+blk\.(0)\.attn_q.*=q8_0
+blk\.(0)\.attn_k.*=q8_0
+blk\.(0)\.attn_v.*=q8_0
+blk\.(0)\.attn_output.*=q8_0
+
+blk\..*\.attn_q.*=iq5_k
+blk\..*\.attn_k.*=iq6_k
+blk\..*\.attn_v.*=iq6_k
+blk\..*\.attn_output.*=iq5_k
+
+# Routed Experts
+blk\.(0|47)\.ffn_down_exps\.weight=q8_0
+blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0
+
+blk\..*\.ffn_down_exps\.weight=iq4_ks
+blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss
+
+# Non-Repeating Layers
+token_embd\.weight=iq4_k
+output\.weight=iq6_k
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ4_KSS.gguf \
+    IQ4_KSS \
+    192
+```
+
+</details>
+
+## `IQ4_KT` 14.438 GiB (4.062 BPW)
+Final estimate: PPL = TODO
+
+Mostly pure IQ4_KT meant for full GPU offload similar to [turboderp-org/exllamav3](https://github.com/turboderp-org/exllamav3) [check out ArtusDev's HuggingFace Page](https://huggingface.co/ArtusDev) for someh excellent EXL3 quants!
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 48 Repeating Layers [0-47]
+
+# Attention
+blk\..*\.attn_q.*=iq4_kt
+blk\..*\.attn_k.*=iq4_kt
+blk\..*\.attn_v.*=iq4_kt
+blk\..*\.attn_output.*=iq4_kt
+
+# Routed Experts
+blk\..*\.ffn_down_exps\.weight=iq4_kt
+blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kt
+
+# Non-Repeating Layers
+token_embd\.weight=iq4_kt
+output\.weight=iq6_k
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ4_KT.gguf \
+    IQ4_KT \
+    192
+```
+
+</details>
+
+## `IQ3_K` 14.509 GiB (4.082 BPW)
+Final estimate: PPL = TODO
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 48 Repeating Layers [0-47]
+
+# Attention
+blk\.(0)\.attn_q.*=q8_0
+blk\.(0)\.attn_k.*=q8_0
+blk\.(0)\.attn_v.*=q8_0
+blk\.(0)\.attn_output.*=q8_0
+
+blk\..*\.attn_q.*=iq5_k
+blk\..*\.attn_k.*=iq6_k
+blk\..*\.attn_v.*=iq6_k
+blk\..*\.attn_output.*=iq5_k
+
+# Routed Experts
+blk\.(0|47)\.ffn_down_exps\.weight=q8_0
+blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0
+
+blk\..*\.ffn_down_exps\.weight=iq4_k
+blk\..*\.ffn_(gate|up)_exps\.weight=iq3_k
+
+# Non-Repeating Layers
+token_embd\.weight=iq4_k
+output\.weight=iq6_k
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ3_K.gguf \
+    IQ3_K \
+    192
+```
+
+</details>
+
+## `IQ3_KS` 13.633 GiB (3.836 BPW)
+Final estimate: PPL = TODO
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 48 Repeating Layers [0-47]
+
+# Attention
+blk\.(0)\.attn_q.*=q8_0
+blk\.(0)\.attn_k.*=q8_0
+blk\.(0)\.attn_v.*=q8_0
+blk\.(0)\.attn_output.*=q8_0
+
+blk\..*\.attn_q.*=iq4_ks
+blk\..*\.attn_k.*=iq5_ks
+blk\..*\.attn_v.*=iq5_ks
+blk\..*\.attn_output.*=iq4_ks
+
+# Routed Experts
+blk\.(0|47)\.ffn_down_exps\.weight=q8_0
+blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0
+
+blk\..*\.ffn_down_exps\.weight=iq4_ks
+blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks
+
+# Non-Repeating Layers
+token_embd\.weight=iq4_k
+output\.weight=iq6_k
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ3_KS.gguf \
+    IQ3_KS \
+    192
+```
+
+</details>
+
+## `IQ2_KL` 11.516 GiB (3.240 BPW)
+Final estimate: PPL = TODO
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 48 Repeating Layers [0-47]
+
+# Attention
+blk\.(0)\.attn_q.*=q8_0
+blk\.(0)\.attn_k.*=q8_0
+blk\.(0)\.attn_v.*=q8_0
+blk\.(0)\.attn_output.*=q8_0
+
+blk\..*\.attn_q.*=iq5_k
+blk\..*\.attn_k.*=iq6_k
+blk\..*\.attn_v.*=iq6_k
+blk\..*\.attn_output.*=iq5_k
+
+# Routed Experts
+blk\.(0|47)\.ffn_down_exps\.weight=q8_0
+blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0
+
+blk\..*\.ffn_down_exps\.weight=iq3_ks
+blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl
+
+# Non-Repeating Layers
+token_embd\.weight=iq4_k
+output\.weight=iq6_k
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ2_KL.gguf \
+    IQ2_KL \
+    192
+```
+
+</details>
+
+## `IQ2_KT` 9.469 GiB (2.664 BPW)
+Final estimate: PPL = TODO
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 48 Repeating Layers [0-47]
+blk\.(0)\.attn_q.*=iq5_ks
+blk\.(0)\.attn_k.*=iq6_k
+blk\.(0)\.attn_v.*=iq6_k
+blk\.(0)\.attn_output.*=iq5_ks
+
+# Attention
+blk\..*\.attn_q.*=iq4_kt
+blk\..*\.attn_k.*=iq5_ks
+blk\..*\.attn_v.*=iq5_ks
+blk\..*\.attn_output.*=iq4_kt
+
+# Routed Experts
+blk\.(0|47)\.ffn_down_exps\.weight=iq4_kt
+blk\.(0|47)\.ffn_(gate|up)_exps\.weight=iq4_kt
+
+blk\..*\.ffn_down_exps\.weight=iq3_kt
+blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kt
+
+# Non-Repeating Layers
+token_embd\.weight=iq4_kt
+output\.weight=iq6_k
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ2_KT.gguf \
+    IQ2_KT \
+    192
+```
+
+</summary>
+
+## `IQ1_KT` 7.583 GiB (2.133 BPW)
+Final estimate: PPL = TODO
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 48 Repeating Layers [0-47]
+blk\.(0)\.attn_q.*=iq5_ks
+blk\.(0)\.attn_k.*=iq6_k
+blk\.(0)\.attn_v.*=iq6_k
+blk\.(0)\.attn_output.*=iq5_ks
+
+# Attention
+blk\..*\.attn_q.*=iq4_kt
+blk\..*\.attn_k.*=iq5_ks
+blk\..*\.attn_v.*=iq5_ks
+blk\..*\.attn_output.*=iq4_kt
+
+# Routed Experts
+blk\.(0|47)\.ffn_down_exps\.weight=iq4_kt
+blk\.(0|47)\.ffn_(gate|up)_exps\.weight=iq4_kt
+
+blk\..*\.ffn_down_exps\.weight=iq2_kt
+blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt
+
+# Non-Repeating Layers
+token_embd\.weight=iq4_kt
+output\.weight=iq6_k
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
+    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ1_KT.gguf \
+    IQ1_KT \
+    192
+```
+
+</details>
+
+## Quick Start
+#### Full GPU Offload with CUDA or Vulkan (for AMD GPUs)
+```bash
+# Compile CUDA backend
+cmake -B ./build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DGGML_SCHED_MAX_COPIES=1 -DGGML_CUDA_F16=ON
+cmake --build ./build --config Release -j $(nproc)
+
+# Compile Vulkan backend
+# Experimental doesn't work with all quant types, need to test some more
+# https://github.com/ikawrakow/ik_llama.cpp/discussions/590
+cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_HIPBLAS=0 -DGGML_VULKAN=1
+cmake --build build --config Release -j $(nproc)
+
+# Run Server
+./build/bin/llama-server \
+    --model Qwen3-Coder-30B-A3B-Instruct-IQ3_KS.gguf \
+    --alias ubergarm/Qwen3-Coder-30B-A3B-Instruct \
+    --ctx-size 32768 \
+    -ctk q8_0 -ctv q8_0 \
+    -fa -fmoe \
+    -ngl 99 \
+    --parallel 1 \
+    --threads 1 \
+    --host 127.0.0.1 \
+    --port 8080
+```
+
+#### CPU-only Backend
+```bash
+# Compile
+cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=0 -DGGML_VULKAN=0
+cmake --build build --config Release -j $(nproc)
+
+# Run Server
+./build/bin/llama-server \
+    --model Qwen3-Coder-30B-A3B-Instruct-IQ3_KS.gguf \
+    --alias ubergarm/Qwen3-Coder-30B-A3B-Instruct \
+    --ctx-size 32768 \
+    -ctk q8_0 -ctv q8_0 \
+    -fa -fmoe \
+    -ub 4096 -b 4096 \
+    --parallel 1 \
+    --threads 8 \
+    --host 127.0.0.1 \
+    --port 8080 \
+    --no-mmap
+```
+
+## References
+* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)
+* [Getting Started Guide (already out of date lol)](https://github.com/ikawrakow/ik_llama.cpp/discussions/258)
+* [ubergarm-imatrix-calibration-corpus-v02.txt](https://gist.github.com/ubergarm/edfeb3ff9c6ec8b49e88cdf627b0711a?permalink_comment_id=5682584#gistcomment-5682584)