{"id":247,"date":"2025-08-22T17:07:27","date_gmt":"2025-08-22T09:07:27","guid":{"rendered":"https:\/\/knoka.vip\/?p=247"},"modified":"2025-08-22T17:07:55","modified_gmt":"2025-08-22T09:07:55","slug":"linux%e7%b3%bb%e7%bb%9f%e5%ae%89%e8%a3%85llama-cpp%e5%b9%b6%e9%83%a8%e7%bd%b2ernie-4-5-0-3b%e5%85%a8%e6%b5%81%e7%a8%8b","status":"publish","type":"post","link":"http:\/\/knoka.vip\/index.php\/2025\/08\/22\/linux%e7%b3%bb%e7%bb%9f%e5%ae%89%e8%a3%85llama-cpp%e5%b9%b6%e9%83%a8%e7%bd%b2ernie-4-5-0-3b%e5%85%a8%e6%b5%81%e7%a8%8b\/","title":{"rendered":"Linux\u7cfb\u7edf\u5b89\u88c5llama-cpp\u5e76\u90e8\u7f72ERNIE-4.5-0.3B\u5168\u6d41\u7a0b"},"content":{"rendered":"\n

 Linux\u7cfb\u7edf\u5b89\u88c5llama-cpp\u3010CPU\u3011<\/h3>\n\n\n\n

\u5982\u679cgit\u62c9\u53d6\u8fc7\u6162\uff0c\u53c8\u4e0d\u77e5\u9053\u600e\u4e48\u914d\u7f6e\u4ee3\u7406\uff0c\u53ef\u4ee5\u4f7f\u7528\u6b64\u7f51\u7ad9\uff1aGithub Proxy \u6587\u4ef6\u4ee3\u7406\u52a0\u901f<\/a><\/p>\n\n\n\n

git clone https:\/\/github.com\/ggml-org\/llama.cpp\ncd llama.cpp\n# \u914d\u7f6e\u6784\u5efa\u7cfb\u7edf\uff08\u751f\u6210 Makefile\uff09\ncmake -B build \n# \u7f16\u8bd1\u51fa\u53ef\u6267\u884c\u6587\u4ef6\ncmake --build build --config Release<\/pre>\n\n\n\n
\u4e0b\u8f7d\u6a21\u578b<\/h3>\n\n\n\n
\u4ecehuggingface\u4e0b\u8f7dERNIE-4.5-0.3B-PT-Q4_K_M\u8fd9\u4e2a\uff1ahttps:\/\/huggingface.co\/unsloth\/ERNIE-4.5-0.3B-PT-GGUF<\/a><\/p>\n\n\n\n
\u4fdd\u5b58\u81f3\u6307\u5b9a\u4f4d\u7f6e\uff0c\u8fd9\u91cc\u5047\u8bbe\u6a21\u578b\u4fdd\u5b58\u81f3\/data\/coding\/ERNIE-4.5-0.3B-PT-Q4_K_M.gguf<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u542f\u52a8\u670d\u52a1<\/h3>\n\n\n\n
\u6ce8\u610fllama-server\u8fd9\u91cc\u7528\u7edd\u5bf9\u8def\u5f84\uff0c\u6a21\u578b\u4e5f\u53ef\u4ee5\u7528\u7edd\u5bf9\u8def\u5f84\uff0c\u76f4\u63a5llama-server\u6216\u8005\u5728bin\u91cc\u9762llama-server\u4f1a\u8bc6\u522b\u4e0d\u51fa\u6765~<\/p>\n\n\n\n
\/data\/coding\/llama.cpp\/build\/bin\/llama-server -m \/data\/coding\/ERNIE-4.5-0.3B-PT-Q4_K_M.gguf --port 8081<\/pre>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u8c03\u7528\u670d\u52a1<\/h3>\n\n\n\n
# pip install openai -i https:\/\/repo.huaweicloud.com\/repository\/pypi\/simple\n\u200b\nfrom openai import OpenAI\n\u200b\n# \u6307\u5411\u672c\u5730\u8fd0\u884c\u7684 llama-server\nclient = OpenAI(\n    base_url=\"http:\/\/localhost:8081\/v1\",  # \u6ce8\u610f\uff1a\u5fc5\u987b\u5305\u542b \/v1\n    api_key=\"not-needed\"  # llama-server \u4e0d\u9700\u8981 API key\uff0c\u4f46\u5fc5\u987b\u4f20\u4e00\u4e2a\u503c\uff08\u4e0d\u80fd\u4e3a None\uff09\n)\n\u200b\n# \u53d1\u8d77\u804a\u5929\u8865\u5168\u8bf7\u6c42\nstream = False  # \u6539\u4e3a True \u53ef\u4ee5\u6d41\u5f0f\u8f93\u51fa\n\u200b\nresponse = client.chat.completions.create(\n    model=\"local-model\",  # \u8fd9\u4e2a\u5b57\u6bb5\u5176\u5b9e\u4f1a\u88ab\u5ffd\u7565\uff0c\u4f46\u5fc5\u987b\u4f20\n    messages=[\n        {\"role\": \"system\", \"content\": \"\u4f60\u662f\u4e00\u4e2a\u4e50\u4e8e\u52a9\u4eba\u7684\u52a9\u624b\u3002\"},\n        {\"role\": \"user\", \"content\": \"\u8bf7\u7528\u4e2d\u6587\u5199\u4e00\u9996\u5173\u4e8e\u79cb\u5929\u7684\u8bd7\u3002\"}\n    ],\n    temperature=0.7,\n    max_tokens=512,\n    stream=stream,\n)\n\u200b\nif stream:\n    for chunk in response:\n        print(chunk.choices[0].delta.content or \"\", end=\"\", flush=True)\nelse:\n    print(response.choices[0].message.content)<\/pre>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u5176\u4ed6\u8865\u5145<\/h3>\n\n\n\n
\u67e5\u770b\u6a21\u578b\u8fd0\u884c\u5728GPU\/CPU<\/h4>\n\n\n\n
\u770b llama-server<\/code> \u542f\u52a8\u65f6\u7684\u8f93\u51fa\u65e5\u5fd7\uff08\u6700\u76f4\u63a5\uff09<\/p>\n\n\n\n
system_info: n_threads = 8                                      # \u4f7f\u7528 8 \u4e2a CPU \u7ebf\u7a0b\nllama.cpp: loading model from \/path\/to\/your-model.gguf\nllama_model_loader: loaded meta data with 11 key-value pairs    # \u6a21\u578b\u5143\u6570\u636e\nllama_model_loader: VBMI 160 gguf file detected\nllama_model_loader: loading model: q4_k - mmp 0.000000         # \u91cf\u5316\u7c7b\u578b\nllama_model_loader:     tensor     0: output          [f32]     4096   32000 - CPU\nllama_model_loader:     tensor     1: token_embd     [f32]     32000    4096 - CPU\nllama_model_loader:     tensor     2: output_norm    [f32]     4096         - CPU\nllama_model_loader:     tensor     3: blk.0.attn_q   [f16]     4096    4096 - GPU\nllama_model_loader:     tensor     4: blk.0.attn_k   [f16]     4096    4096 - GPU\nllama_model_loader:     tensor     5: blk.0.attn_v   [f16]     4096    4096 - GPU\n...\nllama_init_backend: using CUDA backend<\/pre>\n\n\n\n
\u8981\u5224\u65ad\u4f60\u5f53\u524d\u8fd0\u884c\u7684 llama.cpp<\/code> \u6a21\u578b\u662f\u8fd0\u884c\u5728 CPU<\/strong> \u8fd8\u662f GPU\uff08\u5982 CUDA\u3001Vulkan\u3001Metal \u7b49\uff09<\/strong> \u4e0a\uff0c\u4e3b\u8981\u770bllama_init_backend: using CUDA backend\u8fd9\u884c<\/p>\n\n\n\n
\u8f93\u51fa\u5185\u5bb9<\/th>\u542b\u4e49<\/th><\/tr><\/thead>
using CUDA backend<\/code><\/td>\u6b63\u5728\u4f7f\u7528 NVIDIA GPU\uff08CUDA\uff09<\/td><\/tr>
using Metal backend<\/code><\/td>macOS \u4e0a\u4f7f\u7528 Apple GPU\uff08M1\/M2\uff09<\/td><\/tr>
using Vulkan backend<\/code><\/td>\u4f7f\u7528 Vulkan GPU\uff08Linux\/Windows\uff09<\/td><\/tr>
using CPU backend<\/code> \u6216\u6ca1\u663e\u793a backend<\/td>\u4ec5\u4f7f\u7528 CPU<\/td><\/tr>
tensor ... - GPU<\/code><\/td>\u67d0\u4e9b\u5c42\u5df2\u5378\u8f7d\u5230 GPU<\/td><\/tr>
tensor ... - CPU<\/code><\/td>\u8be5\u5c42\u5728 CPU \u4e0a\u8fd0\u884c<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\n
\u2705 \u5982\u679c\u4f60\u770b\u5230 \u90e8\u5206 tensor \u5728 GPU \u4e0a<\/strong>\uff0c\u8bf4\u660e\u662f \u6df7\u5408\u6a21\u5f0f\uff08CPU + GPU\uff09\u63a8\u7406<\/strong>\uff0c\u8fd9\u662f llama.cpp<\/code> \u7684\u5178\u578b\u505a\u6cd5\u3002<\/p>\n<\/blockquote>\n\n\n\n
\u68c0\u67e5\u662f\u5426\u542f\u7528 GPU \u652f\u6301<\/h4>\n\n\n\n
\u68c0\u67e5\u662f\u5426\u542f\u7528\u4e86 CUDA\uff08NVIDIA\uff09\uff0c\u5982\u679c\u7f16\u8bd1\u65f6\u6ca1\u6709\u5f00\u542f\u8fd9\u4e9b\u9009\u9879\uff0c\u5373\u4f7f\u6709 GPU \u4e5f\u65e0\u6cd5\u4f7f\u7528<\/strong>\u3002<\/p>\n\n\n\n
cmake -B build -DGGML_CUDA=ON<\/pre>\n\n\n\n
\ud83d\udc49 \u67e5\u770b\u4f60\u7684\u6784\u5efa\u76ee\u5f55\u662f\u5426\u542f\u7528\u4e86 GPU\uff1a<\/p>\n\n\n\n
grep -i cuda build\/CMakeCache.txt\ngrep -i metal build\/CMakeCache.txt\ngrep -i vulkan build\/CMakeCache.txt<\/pre>\n\n\n\n
\u5982\u679c\u6709\u8f93\u51fa\u7c7b\u4f3c\u5982\u4e0b\uff0c\u8bf4\u660e\u652f\u6301 CUDA\uff0c\u5982\u679c\u4e3aOFF\u5219\u8bf4\u660e\u4e0d\u652f\u6301<\/p>\n\n\n\n
GGML_CUDA:BOOL=ON<\/pre>\n\n\n\n
\u542f\u7528GPU\u7248\u672c<\/h4>\n\n\n\n
\u6ce8\u610f\u8fd9\u91cc\u521b\u5efa\u914d\u7f6e\u65f6\u5019\u9700\u8981\u6839\u636e\u5361\u578b\u6307\u5b9a\u5176CUDA \u8ba1\u7b97\u80fd\u529b\uff0c\u5426\u5219\u540e\u9762\u53ef\u80fd\u62a5\u9519\uff0c\u8fd9\u91cc\u6211\u7528\u7684\u662fRTX8000\uff0c\u5176\u8ba1\u7b97\u80fd\u529b\u4e3asm_75<\/strong>\u3002<\/p>\n\n\n\n
\u5404\u7cfb\u5217 NVIDIA GPU \u5bf9\u5e94\u7684 CMAKE_CUDA_ARCHITECTURES<\/code> \u503c\u5982\u4e0b<\/p>\n\n\n\n
\u8fd9\u662f\u5728\u4f7f\u7528 CMake \u7f16\u8bd1 llama.cpp<\/code> \u5e76\u542f\u7528 CUDA \u65f6\uff0c\u5fc5\u987b\u8bbe\u7f6e\u7684\u5173\u952e\u53c2\u6570\uff0c\u5b83\u544a\u8bc9\u7f16\u8bd1\u5668\u4e3a\u76ee\u6807 GPU \u67b6\u6784\u751f\u6210\u4f18\u5316\u7684\u4ee3\u7801\u3002<\/p>\n\n\n\n
GPU \u7cfb\u5217 (\u4ee3\u53f7)<\/th>\u5e38\u89c1\u578b\u53f7\u4e3e\u4f8b<\/th>\u8ba1\u7b97\u80fd\u529b (Compute Capability)<\/th>CMAKE_CUDA_ARCHITECTURES<\/code> \u503c<\/th><\/tr><\/thead>
Pascal<\/strong><\/td>GTX 1080, GTX 1070, Titan Xp<\/td>6.1<\/td>61<\/code><\/td><\/tr>
Volta<\/strong><\/td>Tesla V100<\/td>7.0<\/td>70<\/code><\/td><\/tr>
Turing<\/strong><\/td>RTX 2080 Ti<\/strong>, RTX 2070<\/strong>, Quadro RTX 8000<\/strong><\/td>7.5<\/td>75<\/code><\/td><\/tr>
Ampere<\/strong><\/td>RTX 3090, RTX 3080, A100<\/td>8.0 \/ 8.6<\/td>80<\/code> \u6216 86<\/code><\/td><\/tr>
Ada Lovelace<\/strong><\/td>RTX 4090, RTX 4080<\/td>8.9<\/td>89<\/code><\/td><\/tr>
Hopper<\/strong><\/td>H100<\/td>9.0<\/td>90<\/code><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
# \u8fdb\u5165 llama.cpp \u76ee\u5f55\ncd \/data\/coding\/llama.cpp\n# \u6e05\u7406\u65e7\u6784\u5efa\uff08\u53ef\u9009\uff09\nrm -rf build\n# \u521b\u5efa\u6784\u5efa\u76ee\u5f55\u5e76\u914d\u7f6e\uff08\u542f\u7528 CUDA\uff09\ncmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=75\n# \u7f16\u8bd1\uff08\u5728 build \u76ee\u5f55\u4e0b\u8fd0\u884c make\uff09\nmake -C build -j$(nproc)\n# \u6216\u8005\u7528 cmake \u547d\u4ee4\u7f16\u8bd1\ncmake --build build --config Release --target all -j$(nproc)<\/pre>\n\n\n\n
\u518d\u542f\u52a8\u6a21\u578b\uff0c\u5c31\u4f1a\u53d1\u73b0\u662f\u5728GPU\u7248\u672c\u90e8\u7f72\u7684\u4e86<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"
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