update readme
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README.md
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README.md
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@ -307,8 +307,7 @@ cd LLaMA-Factory
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pip install -e .[metrics]
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```
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> [!TIP]
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> Extra dependencies available: deepspeed, metrics, unsloth, vllm, bitsandbytes, gptq, awq, aqlm, qwen, quality
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Extra dependencies available: deepspeed, metrics, unsloth, vllm, bitsandbytes, gptq, awq, aqlm, qwen, quality
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<details><summary>For Windows users</summary>
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@ -324,6 +323,9 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec
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### LLaMA Board GUI
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> [!IMPORTANT]
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> LLaMA Board GUI only supports training on a single GPU, please use [CLI](#command-line-interface) for distributed training.
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#### Use local environment
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```bash
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@ -335,7 +337,6 @@ CUDA_VISIBLE_DEVICES=0 python src/train_web.py
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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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-v ./hf_cache:/root/.cache/huggingface/ \
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-v ./data:/app/data \
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@ -353,14 +354,13 @@ docker run --gpus=all \
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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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> Details about volume:
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> * hf_cache: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
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> * data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
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> * output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
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<details><summary>Details about volume</summary>
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> [!WARNING]
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> LLaMA Board GUI does not yet support multi-GPUs training.
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- hf_cache: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
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- data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
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- output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
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</details>
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### Command Line Interface
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@ -377,11 +377,12 @@ If you have trouble with downloading models and datasets from Hugging Face, you
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export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
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```
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> [!TIP]
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> Train the model by specifying a model ID of the ModelScope Hub as the `--model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `modelscope/Llama-2-7b-ms`.
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Train the model by specifying a model ID of the ModelScope Hub as the `--model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `modelscope/Llama-2-7b-ms`.
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## Projects using LLaMA Factory
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<details><summary>Click to show</summary>
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1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
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1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
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1. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [[arxiv]](https://arxiv.org/abs/2308.10526)
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@ -411,6 +412,8 @@ export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
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1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
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1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods.
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</details>
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> [!TIP]
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> If you have a project that should be incorporated, please contact via email or create a pull request.
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27
README_zh.md
27
README_zh.md
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@ -307,8 +307,7 @@ cd LLaMA-Factory
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pip install -e .[metrics]
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```
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> [!TIP]
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> 可选的额外依赖项：deepspeed、metrics、unsloth、vllm、bitsandbytes、gptq、awq、aqlm、qwen、quality
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可选的额外依赖项：deepspeed、metrics、unsloth、vllm、bitsandbytes、gptq、awq、aqlm、qwen、quality
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<details><summary>Windows 用户指南</summary>
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@ -324,6 +323,9 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
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### LLaMA Board 可视化界面
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> [!IMPORTANT]
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> LLaMA Board 可视化界面目前仅支持单 GPU 训练，请使用[命令行接口](#命令行接口)来进行分布式训练。
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#### 使用本地环境
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```bash
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@ -331,7 +333,6 @@ CUDA_VISIBLE_DEVICES=0 python src/train_web.py
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# 或 CUDA_VISIBLE_DEVICES=0 python -m llmtuner.webui.interface
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```
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#### 使用 Docker
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```bash
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@ -354,14 +355,13 @@ docker run --gpus=all \
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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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> 数据卷详情：
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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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> * data：宿主机中存放数据集的文件夹路径。
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> * output：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。
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<details><summary>数据卷详情</summary>
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> [!WARNING]
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> LLaMA Board 可视化界面尚不支持多 GPU 训练。
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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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- data：宿主机中存放数据集的文件夹路径。
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- output：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。
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</details>
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### 命令行接口
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@ -378,11 +378,12 @@ docker compose -f ./docker-compose.yml up -d
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export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
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```
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> [!TIP]
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> 将 `--model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型，例如 `modelscope/Llama-2-7b-ms`。
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将 `--model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型，例如 `modelscope/Llama-2-7b-ms`。
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## 使用了 LLaMA Factory 的项目
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<details><summary>点击显示</summary>
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1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
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1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
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1. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [[arxiv]](https://arxiv.org/abs/2308.10526)
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@ -412,6 +413,8 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
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1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: 医疗大模型项目 CareGPT，基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。
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1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**：MBTI性格大模型项目，根据数据集与训练方式让任意 LLM 拥有 16 个不同的性格类型。
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</details>
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> [!TIP]
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> 如果您有项目希望添加至上述列表，请通过邮件联系或者创建一个 PR。
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