2024-01-01-【记录】win10平台6G显存运行Qwen-1-8B.md 8.2 KB


title: 【记录】win10平台6G显存运行Qwen-1.8B urlname: Running-Qwen-on-the-Win10-platform-with-6GB-of-video-memory index_img: https://api.limour.top/randomImg?d=2024-01-01 03:11:36 date: 2024-01-01 11:11:36

tags: llama

Llama.cpp 能 CPU & GPU 环境混合推理,这里记录一下在 windows10 平台上运行 Qwen-1.8B 的过程,显卡是 1660Ti 。

准备模型

  • 安装conda
  • Tun模式(管理员权限)

    conda create -n llamaConvert python=3.10 git -c conda-forge
    conda activate llamaConvert
    cd D:\llama
    git clone --depth=1 https://github.com/ggerganov/llama.cpp.git
    cd llama.cpp
    python -m pip install -r requirements.txt
    pip install tiktoken
    
    python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='Qwen/Qwen-1_8B-Chat', local_dir=r'D:\qwen', ignore_patterns=['*.h5', '*.ot', '*.msgpack', '*.safetensors'])"
    cd D:\qwen
    D:\aria2\aria2c.exe --all-proxy='http://127.0.0.1:7890' -o 'model-00001-of-00002.safetensors' "https://huggingface.co/Qwen/Qwen-1_8B-Chat/resolve/main/model-00001-of-00002.safetensors?download=true"
    D:\aria2\aria2c.exe --all-proxy='http://127.0.0.1:7890' -o 'model-00002-of-00002.safetensors' "https://huggingface.co/Qwen/Qwen-1_8B-Chat/resolve/main/model-00002-of-00002.safetensors?download=true"
    
    cd D:\llama\llama.cpp
    python convert-hf-to-gguf.py D:\qwen
    # Model successfully exported to 'D:\qwen\ggml-model-f16.gguf'
    

    运行模型

  • 下载 llama-b1732-bin-win-cublas-cu12.2.0-x64.zip

  • 提取文件到 D:\llama

    conda create -n llamaCpp libcublas cuda-toolkit git -c nvidia -c conda-forge
    conda activate llamaCpp
    cd D:\llama ; .\main.exe ## 检查能否正确运行
    cd D:\llama ; .\quantize.exe --help ## 自己决定量化方式
    .\quantize.exe D:\qwen\ggml-model-f16.gguf .\qwen-1_8-f16.gguf COPY
    .\server.exe -m .\qwen-1_8-f16.gguf -c 4096 --n-gpu-layers 50 ## 调节 n-gpu-layers 平衡 CPU & GPU
    
  • 访问 http://127.0.0.1:8080 选择 Completion 进行测试

    微调模型

  • h-corpus数据集

  • 官方微调教程

    附加 Yi-6B-Chat

    Yi-6B是零一万物开源的双语语言模型,经过3T多语种语料库的训练,在语言理解、常识推理、阅读理解等方面有一定潜力。

    cd D:\models\01yi
    D:\aria2\aria2c.exe --all-proxy='http://127.0.0.1:7890' -o 'model-00001-of-00003.safetensors' "https://huggingface.co/01-ai/Yi-6B-Chat/resolve/main/model-00001-of-00003.safetensors?download=true"
    D:\aria2\aria2c.exe --all-proxy='http://127.0.0.1:7890' -o 'model-00002-of-00003.safetensors' "https://huggingface.co/01-ai/Yi-6B-Chat/resolve/main/model-00002-of-00003.safetensors?download=true"
    D:\aria2\aria2c.exe --all-proxy='http://127.0.0.1:7890' -o 'model-00003-of-00003.safetensors'  https://huggingface.co/01-ai/Yi-6B-Chat/resolve/main/model-00003-of-00003.safetensors?download=true
    conda activate llamaConvert
    python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='01-ai/Yi-6B-Chat', local_dir=r'D:\models\01yi', ignore_patterns=['*.h5', '*.ot', '*.msgpack', '*.safetensors'])"
    
    conda activate llamaConvert
    cd D:\llama\llama.cpp
    python convert.py D:\models\01yi
    # Wrote D:\models\01yi\ggml-model-f16.gguf
    conda activate llamaCpp
    cd D:\llama ; .\quantize.exe --help
    .\quantize.exe D:\models\01yi\ggml-model-f16.gguf .\01yi-6b-Q4_K_M.gguf Q4_K_M
    .\server.exe -m .\01yi-6b-Q4_K_M.gguf -c 4096 --n-gpu-layers 50
    

    附加 百川2

    cd D:\models\baichuan
    D:\aria2\aria2c.exe --all-proxy='http://127.0.0.1:7890' -o 'pytorch_model.bin' "https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/resolve/main/pytorch_model.bin?download=true"
    conda activate llamaConvert
    python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='baichuan-inc/Baichuan2-7B-Chat', local_dir=r'D:\models\baichuan', ignore_patterns=['*.h5', '*.bin', '*.ot', '*.msgpack', '*.safetensors'])"
    cd D:\llama\llama.cpp
    python convert.py D:\models\baichuan
    # Wrote D:\models\baichuan\ggml-model-f16.gguf
    conda activate llamaCpp
    cd D:\llama ; .\quantize.exe --help
    .\quantize.exe D:\models\baichuan\ggml-model-f16.gguf .\baichuan-7b-Q3_K_M.gguf Q3_K_M
    .\server.exe -m .\baichuan-7b-Q3_K_M.gguf -c 2048 --n-gpu-layers 30
    

    附加 tigerbot-13b

    tigerbot-13bchinese-llm-benchmark 上排名靠前。

    cd D:\models\tigerbot
    D:\aria2\aria2c.exe --all-proxy='http://127.0.0.1:7890' -o 'pytorch_model-00001-of-00003.bin' --max-download-limit=6M "https://huggingface.co/TigerResearch/tigerbot-13b-chat-v5/resolve/main/pytorch_model-00001-of-00003.bin?download=true"
    D:\aria2\aria2c.exe --all-proxy='http://127.0.0.1:7890' -o 'pytorch_model-00002-of-00003.bin' --max-download-limit=6M "https://huggingface.co/TigerResearch/tigerbot-13b-chat-v5/resolve/main/pytorch_model-00002-of-00003.bin?download=true"
    D:\aria2\aria2c.exe --all-proxy='http://127.0.0.1:7890' -o 'pytorch_model-00003-of-00003.bin' --max-download-limit=6M "https://huggingface.co/TigerResearch/tigerbot-13b-chat-v5/resolve/main/pytorch_model-00003-of-00003.bin?download=true"
    conda activate llamaConvert
    python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='TigerResearch/tigerbot-13b-chat-v5', local_dir=r'D:\models\tigerbot', ignore_patterns=['*.h5', '*.bin', '*.ot', '*.msgpack', '*.safetensors'])"
    cd D:\llama\llama.cpp
    python convert.py D:\models\tigerbot --padvocab
    cd D:\llama ; .\quantize.exe --help
    .\quantize.exe D:\models\tigerbot\ggml-model-f16.gguf D:\models\tigerbot-13B-Chat-Q4_K_M.gguf Q4_K_M
    .\server.exe -m D:\models\tigerbot-13B-Chat-Q4_K_M.gguf -c 4096
    

    {% note info %} 感觉 6G 显存下,比较好用的是 Yi-6B-Chat-Q4_K_M tigerbot-13b 在 R5 5600H 上推理速度 4.6 tokens/s,CPU 使用率 60%,频率 3.5GHz,应该是内存带宽瓶颈 {% endnote %}

附加 在 Colab 上量化

  • llm2gguf.ipynb
  • 量化后的结果

    安装 llama.cpp

    !git clone --depth=1 https://github.com/ggerganov/llama.cpp.git
    %cd /content/llama.cpp
    !LLAMA_CUDA=1 make -j
    

    计算 imatrix

    %cd /content
    !wget -O transient.txt.gz https://huggingface.co/datasets/Limour/b-corpus/resolve/main/00-preview/00-transient.txt.gz?download=true
    !gunzip transient.txt.gz
    !mkdir -p /content/CausalLM-14B-GGUF
    !wget -O /content/CausalLM-14B-GGUF/causallm_14b.Q8_0.gguf  https://huggingface.co/TheBloke/CausalLM-14B-GGUF/resolve/main/causallm_14b.Q8_0.gguf?download=true
    !/content/llama.cpp/imatrix -m /content/CausalLM-14B-GGUF/causallm_14b.Q8_0.gguf -f /content/transient.txt -ngl 36
    

    登录拥抱脸

    from google.colab import userdata
    from huggingface_hub import login
    # login(token=os.environ.get("HF_TOKEN"), write_permission=True)
    login(token=userdata.get('HF_TOKEN'), write_permission=True)
    # from huggingface_hub import notebook_login
    # notebook_login()
    

    (跳过) 转换模型

    %cd llama.cpp
    !python -m pip install -r requirements.txt
    !pip install tiktoken
    from huggingface_hub import snapshot_download
    !mkdir -p ~/CausalLM
    snapshot_download(repo_id='CausalLM/7B', local_dir=r'/content/CausalLM', ignore_patterns=['*.h5', '*.ot', '*.msgpack', '*.safetensors'])
    !python convert.py --vocab-type bpe --pad-vocab --outtype f16 /content/CausalLM 
    

    量化模型

    !/content/llama.cpp/quantize --allow-requantize --imatrix /content/imatrix.dat /content/CausalLM-14B-GGUF/causallm_14b.Q8_0.gguf /content/CausalLM-14B-GGUF/causallm_14b.IQ3_XS.gguf IQ3_XS
    

    上传模型

    from huggingface_hub import HfApi
    api = HfApi()
    api.upload_file(
    path_or_fileobj="/content/CausalLM-14B-GGUF/causallm_14b.IQ3_XS.gguf",
    path_in_repo="causallm_14b.IQ3_XS.gguf",
    repo_id="Limour/CausalLM-14B-GGUF"
    )