text: It supports several LLMs.This notebook goes over how to run llama-cpp within LangChain.Installationâ€‹There is a bunch of options how to install the llama-cpp package: only CPU usageCPU + GPU (using one of many BLAS backends)Metal GPU (MacOS with Apple Silicon Chip) CPU only installationâ€‹pip install llama-cpp-pythonInstallation with OpenBLAS / cuBLAS / CLBlastâ€‹lama.cpp supports multiple BLAS backends for faster processing. Use the FORCE_CMAKE=1 environment variable to force the use of cmake and install the pip package for the desired BLAS backend (source).Example installation with cuBLAS backend:CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-pythonIMPORTANT: If you have already installed a cpu only version of the package, you need to reinstall it from scratch: consider the following command: CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cache-dirInstallation with Metalâ€‹lama.cpp supports Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks. Use the FORCE_CMAKE=1 environment variable to force the use of cmake and install the pip package for the Metal support (source).Example installation with Metal Support:CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install llama-cpp-pythonIMPORTANT: If you have already installed a cpu only version of the package, you need to reinstall it from scratch: consider the following command: CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cache-dirInstallation with Windowsâ€‹It is stable to install the llama-cpp-python library by compiling from the source. You can follow most