如果仅在 CPU 上运行,可以直接使用 pip install llama-cpp-python 进行安装。 否则,请确保系统已安装 CUDA,可以通过 nvcc --version 检查。 GGUF 以bartowski/Mistral-7B-Instruct-v0.3-GGUF 为例进行演示。你将在模型界面查看到以下信息:可以看到 4-bit 量化有 IQ4_XS,Q4_K_S, IQ4_NL,Q4_K_M 四种,...
pip install llama-cpp-python \ --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/<cuda-version> Where<cuda-version>is one of the following: cu121: CUDA 12.1 cu122: CUDA 12.2 cu123: CUDA 12.3 cu124: CUDA 12.4 cu125: CUDA 12.5 For example, to install the CUDA 12.1 ...
━━━ 371.5/371.5 kB 40.9 MB/s eta 0:00:00 Ignoring llama-cpp-python-cuda: markers 'platform_system == "Windows"' don't match your environment ERROR: llama_cpp_python_cuda-0.2.6+cu117-cp310-cp310-manylinux_2_31_x86_64.whl is not a supported wheel on this platform. (textgen) ...
llama_kv_cache_init: CUDA0 KV buffer size = 328.00 MiB llama_kv_cache_init: CUDA1 KV buffer size = 312.00 MiB llama_new_context_with_model: KV self size = 640.00 MiB, K (f16): 320.00 MiB, V (f16): 320.00 MiB llama_new_context_with_model: CUDA_Host output buffer size = 0.52 M...
| NVIDIA-SMI 545.29.01 Driver Version: 546.01 CUDA Version: 12.3 | |---+---+---+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===+===...
--extra-index-url=https://abetlen.github.io/llama-cpp-python/whl/$CUDA_VERSION \ llama-cpp-python # 对于 Metal (MPS) export GGML_METAL=on pip install llama-cpp-python 运行示例 安装完成后,你可以通过下面的命令来测试 Llama-CPP-Python 是否正确安装: ...
ARG CUDA_VERSION=12.6.0 # Target the CUDA build image ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} # Target the CUDA runtime image ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} ...
CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python Pre-built Wheel (New) It is also possible to install a pre-built wheel with CUDA support. As long as your system meets some requirements: CUDA Version is 12.1, 12.2, 12.3, 12.4 or 12.5 ...
llama_model_load_internal：using CUDA for GPU acceleration llama_model_load_internal：所需内存= 238...
After that I tried to do the installation with the flags. Since I have CUDA version 12.2, I corrected CUDA to 12.2 in the flags: set "CMAKE_ARGS=-Tv143,cuda=12.2 -DLLAMA_CUBLAS=on". After uninstalling and using pip uninstall llama-cpp-python ...