完成以上步骤后,我们需要进行功能测试以确保一切正常工作。 importtorchdeftest_llama():device="cuda"iftorch.cuda.is_available()else"cpu"model=LlamaModel().to(device)output=model(input_data)assertoutputisnotNone,"Model output is None!"
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,...
4、当我们没有那么多显存足以加载整个gguf模型,就得分一部分给CPU进行加载推理了 from llama_cpp import Llama import json from tqdm import tqdm llm =Llama(model_path="Qwen2-72B-Instruct-Q4_K_M.gguf",n_gpu_layers=20, chat_format='qwen', n_ctx=2048) datas = json.load(open("test.json",...
pip install llama-cpp-python==0.3.2 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu 使用pip 安装 GPU 版本(以 cuBLAS 为例) 如果您有 NVIDIA GPU 并希望使用 cuBLAS 后端,可以设置环境变量并安装: bash CMAKE_ARGS="-DLLAMA_CUBLAS=ON" pip install llama-cpp-python 在...
importllama_cpp_python# 创建一个GPU上的Tensortensor=llama_cpp_python.GPUTensor(shape=(3,3),device=device)# 执行Tensor的操作tensor.fill(0.5)tensor.mul(2.0)# 将Tensor复制到CPU并打印结果print(tensor.to_cpu()) 1. 2. 3. 4. 5. 6.
pip install llama-cpp-python \ --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu Installation Configuration llama.cpp supports a number of hardware acceleration backends to speed up inference as well as backend specific options. See the llama.cpp README for a full list....
低级API 直接ctypes绑定到llama.cpp. 整个低级 API 可以在llama_cpp/llama_cpp.py中找到,并直接镜像llama.h中的 C API 。 代码语言:text AI代码解释 import llama_cpp import ctypes params = llama_cpp.llama_context_default_params() # use bytes for char * params ...
OpenBLAS (CPU) To install with OpenBLAS, set theGGML_BLASandGGML_BLAS_VENDORenvironment variables before installing: CMAKE_ARGS="-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS"pip install llama-cpp-python CUDA To install with CUDA support, set theGGML_CUDA=onenvironment variable before installing...
pip install llama-cpp-python \ --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu Installation Configuration llama.cppsupports a number of hardware acceleration backends to speed up inference as well as backend specific options. See thellama.cpp READMEfor a full list. ...
feat: Binary wheels for CPU, CUDA (12.1 - 12.3), Metal by @abetlen, @jllllll, and @oobabooga in #1247 fix: segfault when logits_all=False by @abetlen in 8649d7671bd1a7c0d9cc6a5ad91c6ca286512ab3 fix: last tokens passing to sample_repetition_penalties function by @ymikhailov in...