Code LLaMA Deploy

环境准备

建议系统环境和配置信息如下：

4 vCPU 24GB Memory 1GPU
Ubuntu 20.04.2 LTS
Python >= 3.8.5
pytorch >= 1.12

下载模型文件，使用的模型文件是 codefuse-codellama-34b.Q4_K_M.gguf，大小为 20.2G。

wget https://huggingface.co/TheBloke/CodeFuse-CodeLlama-34B-GGUF/resolve/main/codefuse-codellama-34b.Q4_K_M.gguf?download=true

创建 python 虚拟环境

python -m venv venv-codellma1

激活 python 虚拟环境

source ./venv-codellma1/bin/activate

运行 llama-cpp-python

llama-cpp-python 提供了一个 Web 服务器，旨在充当 OpenAI API 的替代品。这允许您将 llama.cpp 兼容模型与任何 OpenAI 兼容客户端（语言库、服务等）一起使用。

安装 llama-cpp-python

# 环境变量 CMAKE_ARGS="-DLLAMA_CUBLAS=on" 是为了使用 cuBLAS 库，使用GPU加速模型推理
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python-server

启动 llama-cpp-python 服务成功后，导航到 http://localhost:8080/docs 查看 OpenAPI 文档，也可以查看 swagger api json。

# --model: 模型文件路径
# --n_gpu_layers: 指定模型的多少层使用GPU进行推理
# --n_ctx: 为模型的最大上下文长度

python -m llama_cpp.server --model /mnt/data/codefuse-codellama-34b.Q4_K_M.gguf --n_gpu_layers 100 --host 0.0.0.0 --port 8080 --n_ctx 2048

启动如果遇到 Cannot allocate memory 错误，解决方案是先执行命令 export USE_MLOCK=0

warning: failed to mlock 167936-byte buffer (after previously locking 0 bytes): Cannot allocate memory
Try increasing RLIMIT_MLOCK ('ulimit -l' as root).

启动成功之后，输出如下信息：

llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token  = 13 '<0x0A>'
llm_load_tensors: ggml ctx size =    0.16 MiB
llm_load_tensors: mem required  = 19282.64 MiB
...................................................................................................
llama_new_context_with_model: n_ctx      = 2048
llama_new_context_with_model: freq_base  = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: kv self size  =  384.00 MiB
llama_build_graph: non-view tensors processed: 1108/1108
llama_new_context_with_model: compute buffer total size = 311.07 MiB
AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 |
INFO:     Started server process [1034]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)

发送测试请求

curl -X POST --header 'Content-Type: application/json' http://localhost:8080/v1/chat/completions --data '{"messages":[{"role":"user", "content":"def sort(d):"}]}'
{
    "id": "chatcmpl-01e291d2-1166-4dc4-848d-3243ee999261",
    "object": "chat.completion",
    "created": 1700743684,
    "model": "/mnt/data/codefuse-codellama-34b.Q4_K_M.gguf",
    "choices": [
        {
            "index": 0,
            "message": {
                "content": "\n    k = list(d.keys())\n    v = list(d.values())\n    for i in range(len(k)):\n        for j in range(i+1, len(k)):\n            if v[i] < v[j]:\n                v[i], v[j] = v[j], v[i] # swap\n                k[i], k[j] = k[j], k[i]\n    return dict(zip(k, v)) \n    \nd = {'apple': 10, 'banana': 20, 'orange': 5, 'pear': 15}\nprint(sort(d))",
                "role": "assistant"
            },
            "finish_reason": "stop"
        }
    ],
    "usage": {
        "prompt_tokens": 16,
        "completion_tokens": 152,
        "total_tokens": 168
    }
}

将响应参数中的 content 字段值取出来，用 python 执行，输出结果如下：