parent
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commit
949e9908ad
38
Dockerfile
38
Dockerfile
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@ -1,14 +1,44 @@
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FROM nvcr.io/nvidia/pytorch:24.01-py3
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# Use the NVIDIA official image with PyTorch 2.3.0
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# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-02.html
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FROM nvcr.io/nvidia/pytorch:24.02-py3
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# Define installation arguments
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ARG INSTALL_BNB=false
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ARG INSTALL_VLLM=false
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ARG INSTALL_DEEPSPEED=false
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ARG PIP_INDEX=https://pypi.org/simple
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# Set the working directory
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WORKDIR /app
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# Install the requirements
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COPY requirements.txt /app/
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RUN pip install -r requirements.txt
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RUN pip config set global.index-url $PIP_INDEX
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RUN python -m pip install --upgrade pip
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RUN python -m pip install -r requirements.txt
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# Copy the rest of the application into the image
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COPY . /app/
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RUN pip install -e .[metrics,bitsandbytes,qwen]
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# Install the LLaMA Factory
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RUN EXTRA_PACKAGES="metrics"; \
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if [ "$INSTALL_BNB" = "true" ]; then \
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EXTRA_PACKAGES="${EXTRA_PACKAGES},bitsandbytes"; \
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fi; \
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if [ "$INSTALL_VLLM" = "true" ]; then \
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EXTRA_PACKAGES="${EXTRA_PACKAGES},vllm"; \
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fi; \
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if [ "$INSTALL_DEEPSPEED" = "true" ]; then \
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EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
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fi; \
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pip install -e .[$EXTRA_PACKAGES] && \
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pip uninstall -y transformer-engine
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# Set up volumes
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VOLUME [ "/root/.cache/huggingface/", "/app/data", "/app/output" ]
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# Expose port 7860 for the LLaMA Board
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EXPOSE 7860
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CMD [ "llamafactory-cli", "webui" ]
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# Expose port 8000 for the API service
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EXPOSE 8000
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36
README.md
36
README.md
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@ -405,9 +405,9 @@ Please refer to [data/README.md](data/README.md) for checking the details about
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Use the following 3 commands to run LoRA **fine-tuning**, **inference** and **merging** of the Llama3-8B-Instruct model, respectively.
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
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llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
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llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
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llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
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```
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See [examples/README.md](examples/README.md) for advanced usage (including distributed training).
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### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))
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#### Use local environment
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```bash
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CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
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llamafactory-cli webui
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```
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</details>
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#### Use Docker
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### Build Docker
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```bash
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docker build -f ./Dockerfile -t llama-factory:latest .
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docker run --gpus=all \
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docker build -f ./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 ./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 llama_factory \
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-d llama-factory:latest
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--name llamafactory \
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llamafactory:latest
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```
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#### Use Docker Compose
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```bash
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docker compose -f ./docker-compose.yml up -d
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```
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> [!TIP]
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> Use Docker Compose to build image via `docker compose up -d`.
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<details><summary>Details about volume</summary>
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34
README_zh.md
34
README_zh.md
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@ -405,9 +405,9 @@ Docker 镜像:
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下面三行命令分别对 Llama3-8B-Instruct 模型进行 LoRA **微调**、**推理**和**合并**。
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
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llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
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llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
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llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
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```
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高级用法请参考 [examples/README_zh.md](examples/README_zh.md)(包括多 GPU 微调)。
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### LLaMA Board 可视化微调(由 [Gradio](https://github.com/gradio-app/gradio) 驱动)
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#### 使用本地环境
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```bash
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CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
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llamafactory-cli webui
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```
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#### 使用 Docker
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### 构建 Docker
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```bash
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docker build -f ./Dockerfile -t llama-factory:latest .
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docker run --gpus=all \
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docker build -f ./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 ./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 llama_factory \
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-d llama-factory:latest
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--name llamafactory \
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llamafactory:latest
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```
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#### 使用 Docker Compose
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```bash
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docker compose -f ./docker-compose.yml up -d
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```
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> [!TIP]
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> 通过 `docker compose up -d` 使用 Docker Compose 构建镜像。
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<details><summary>数据卷详情</summary>
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version: '3.8'
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services:
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llama-factory:
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llamafactory:
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build:
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dockerfile: Dockerfile
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context: .
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container_name: llama_factory
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args:
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INSTALL_BNB: false
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INSTALL_VLLM: false
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INSTALL_DEEPSPEED: false
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PIP_INDEX: https://pypi.org/simple
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container_name: llamafactory
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volumes:
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- ./hf_cache:/root/.cache/huggingface/
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- ./data:/app/data
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- ./output:/app/output
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ports:
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- "7860:7860"
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- "8000:8000"
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ipc: host
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deploy:
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resources:
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