text: 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="./ggml-model-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,)The rest are almost same as GPU, the console log will show the following log to indicate the Metal was enable properly.ggml_metal_init: allocatingggml_metal_init: using MPS...You also could check the Activity Monitor by watching the % GPU of the process, the % CPU will drop dramatically after turn on n_gpu_layers=1. Also for the first time call LLM, the performance might be slow due to the model compilation in Metal GPU.PreviousKoboldAI APINextCaching integrationsInstallationCPU only installationInstallation with OpenBLAS / cuBLAS / CLBlastInstallation with MetalInstallation with WindowsUsageCPUGPUMetalCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.