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README_zh.md
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README_zh.md
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@ -360,7 +360,7 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
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<details><summary>昇腾 NPU 用户指南</summary>
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在昇腾 NPU 设备上安装 LLaMA Factory 时，需要指定额外依赖项，使用 `pip install -e '.[torch-npu,metrics]'` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
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在昇腾 NPU 设备上安装 LLaMA Factory 时，需要指定额外依赖项，使用 `pip install -e ".[torch-npu,metrics]"` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
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```bash
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# 请替换 URL 为 CANN 版本和设备型号对应的 URL
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@ -383,12 +383,6 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
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| torch-npu | 2.1.0 | 2.1.0.post3 |
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| deepspeed | 0.13.2 | 0.13.2 |
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Docker用户请参考 [构建 Docker](#构建-Docker).
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**NOTE**
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默认镜像为 [cosdt/cann:8.0.rc1-910b-ubuntu22.04](https://hub.docker.com/layers/cosdt/cann/8.0.rc1-910b-ubuntu22.04/images/sha256-29ef8aacf6b2babd292f06f00b9190c212e7c79a947411e213135e4d41a178a9?context=explore). 更多选择见 [cosdt/cann](https://hub.docker.com/r/cosdt/cann/tags).
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请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。
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如果遇到无法正常推理的情况，请尝试设置 `do_sample: false`。
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@ -425,49 +419,62 @@ llamafactory-cli webui
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### 构建 Docker
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#### 使用 Docker
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<details><summary>NVIDIA GPU 用户：</summary>
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CUDA 用户：
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```bash
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cd ./docker/docker-cuda
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docker build -f ./Dockerfile \
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docker-compose -f ./docker/docker-cuda/docker-compose.yml up -d
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docker-compose exec llamafactory bash
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```
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昇腾 NPU 用户：
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```bash
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docker-compose -f ./docker/docker-npu/docker-compose.yml up -d
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docker-compose exec llamafactory bash
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```
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<details><summary>不使用 Docker Compose 构建</summary>
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CUDA 用户：
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```bash
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docker build -f ./docker/docker-cuda/Dockerfile \
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--build-arg INSTALL_BNB=false \
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--build-arg INSTALL_VLLM=false \
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--build-arg INSTALL_DEEPSPEED=false \
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--build-arg PIP_INDEX=https://pypi.org/simple \
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-t llamafactory:latest .
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docker run -it --gpus=all \
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-v /$(dirname $(dirname "$PWD"))/hf_cache:/root/.cache/huggingface/ \
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-v /$(dirname $(dirname "$PWD"))/data:/app/data \
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-v /$(dirname $(dirname "$PWD"))/output:/app/output \
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docker run -dit --gpus=all \
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-v ./hf_cache:/root/.cache/huggingface/ \
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-v ./data:/app/data \
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-v ./output:/app/output \
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-p 7860:7860 \
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-p 8000:8000 \
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--shm-size 16G \
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--name llamafactory \
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llamafactory:latest
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```
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</details>
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<details><summary>Ascend NPU 用户：</summary>
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docker exec -it llamafactory bash
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```
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昇腾 NPU 用户：
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```bash
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cd ./docker/docker-npu
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docker build -f ./Dockerfile \
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# 根据您的环境选择镜像
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docker build -f ./docker/docker-npu/Dockerfile \
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--build-arg INSTALL_DEEPSPEED=false \
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--build-arg PIP_INDEX=https://pypi.org/simple \
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-t llamafactory:latest .
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# 增加 --device 来使用多卡 NPU 或修改第一个 --device 来更改 NPU 卡
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docker run -it \
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-v /$(dirname $(dirname "$PWD"))/hf_cache:/root/.cache/huggingface/ \
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-v /$(dirname $(dirname "$PWD"))/data:/app/data \
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-v /$(dirname $(dirname "$PWD"))/output:/app/output \
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# 根据您的资源更改 `device`
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docker run -dit \
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-v ./hf_cache:/root/.cache/huggingface/ \
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-v ./data:/app/data \
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-v ./output:/app/output \
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-v /usr/local/dcmi:/usr/local/dcmi \
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-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
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-v /usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64 \
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-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
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-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
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-v /etc/ascend_install.info:/etc/ascend_install.info \
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-p 7860:7860 \
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-p 8000:8000 \
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@ -478,26 +485,12 @@ docker run -it \
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--shm-size 16G \
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--name llamafactory \
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llamafactory:latest
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docker exec -it llamafactory bash
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```
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</details>
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#### 使用 Docker Compose
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首先进入 docker 目录：
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```bash
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# NVIDIA GPU 用户
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cd ./docker/docker-cuda
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# Ascend NPU 用户
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cd ./docker/docker-npu
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```
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然后运行以下命令创建 docker 镜像并启动容器:
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```bash
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docker-compose up -d
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docker-compose exec llamafactory bash
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```
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<details><summary>数据卷详情</summary>
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- hf_cache：使用宿主机的 Hugging Face 缓存文件夹，允许更改为新的目录。
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