GGML_ASSERT: .../ggml-metal.m:706: false && "not implemented"Setting these parameters correctly will dramatically improve the evaluation speed (see wrapper code for more details).n_gpu_layers = 1  # Metal set to 1 is enough.n_batch = 512  # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.# Make sure the model path is correct for your system!llm = LlamaCpp(    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",    n_gpu_layers=n_gpu_layers,    n_batch=n_batch,    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls    callback_manager=callback_manager,    verbose=True, # Verbose is required to pass to the callback manager)The console log will show the following log to indicate Metal was enable properly.ggml_metal_init: allocatingggml_metal_init: using MPS...You also could check Activity Monitor by watching the GPU usage of the process, the CPU usage will drop dramatically after turn on n_gpu_layers=1. For the first call to the LLM, the performance may be slow due to the model compilation in Metal GPU.Grammars‚ÄãWe can use grammars to constrain model outputs and sample tokens based on the rules defined in them.To demonstrate this concept, we've included sample grammar files, that will be used in the examples below.Creating gbnf grammar files can be time-consuming, but if you have a use-case where output schemas are important, there are two tools that can help:Online grammar generator app that converts TypeScript interface definitions to gbnf file.Python script for converting json schema to gbnf file. You can for example create pydantic object, generate its JSON schema using .schema_json() method, and then use this script to convert it to gbnf file.In the first example, supply the path to the specifed json.gbnf file in order to produce JSON:n_gpu_layers = 1  # Metal set to 1 is enough.n_batch = 512  # Should be between 1 and n_ctx, consider the amount
llama-cpp-python is a Python binding for llama.cpp.
llama-cpp-python is a Python binding for llama.cpp. ->: GGML_ASSERT: .../ggml-metal.m:706: false && "not implemented"Setting these parameters correctly will dramatically improve the evaluation speed (see wrapper code for more details).n_gpu_layers = 1  # Metal set to 1 is enough.n_batch = 512  # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.# Make sure the model path is correct for your system!llm = LlamaCpp(    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",    n_gpu_layers=n_gpu_layers,    n_batch=n_batch,    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls    callback_manager=callback_manager,    verbose=True, # Verbose is required to pass to the callback manager)The console log will show the following log to indicate Metal was enable properly.ggml_metal_init: allocatingggml_metal_init: using MPS...You also could check Activity Monitor by watching the GPU usage of the process, the CPU usage will drop dramatically after turn on n_gpu_layers=1. For the first call to the LLM, the performance may be slow due to the model compilation in Metal GPU.Grammars‚ÄãWe can use grammars to constrain model outputs and sample tokens based on the rules defined in them.To demonstrate this concept, we've included sample grammar files, that will be used in the examples below.Creating gbnf grammar files can be time-consuming, but if you have a use-case where output schemas are important, there are two tools that can help:Online grammar generator app that converts TypeScript interface definitions to gbnf file.Python script for converting json schema to gbnf file. You can for example create pydantic object, generate its JSON schema using .schema_json() method, and then use this script to convert it to gbnf file.In the first example, supply the path to the specifed json.gbnf file in order to produce JSON:n_gpu_layers = 1  # Metal set to 1 is enough.n_batch = 512  # Should be between 1 and n_ctx, consider the amount