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hiyouga
2024-04-02 20:22:11 +08:00
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README.md
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@@ -307,8 +307,7 @@ cd LLaMA-Factory
pip install -e .[metrics]
```
> [!TIP]
> Extra dependencies available: deepspeed, metrics, unsloth, vllm, bitsandbytes, gptq, awq, aqlm, qwen, quality
Extra dependencies available: deepspeed, metrics, unsloth, vllm, bitsandbytes, gptq, awq, aqlm, qwen, quality
<details><summary>For Windows users</summary>
@@ -324,6 +323,9 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec
### LLaMA Board GUI
> [!IMPORTANT]
> LLaMA Board GUI only supports training on a single GPU, please use [CLI](#command-line-interface) for distributed training.
#### Use local environment
```bash
@@ -335,7 +337,6 @@ CUDA_VISIBLE_DEVICES=0 python src/train_web.py
```bash
docker build -f ./Dockerfile -t llama-factory:latest .
docker run --gpus=all \
-v ./hf_cache:/root/.cache/huggingface/ \
-v ./data:/app/data \
@@ -353,14 +354,13 @@ docker run --gpus=all \
docker compose -f ./docker-compose.yml up -d
```
> [!TIP]
> Details about volume:
> * hf_cache: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
> * data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
> * output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
<details><summary>Details about volume</summary>
> [!WARNING]
> LLaMA Board GUI does not yet support multi-GPUs training.
- hf_cache: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
- data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
- output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
</details>
### Command Line Interface
@@ -377,11 +377,12 @@ If you have trouble with downloading models and datasets from Hugging Face, you
export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
```
> [!TIP]
> 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`.
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`.
## Projects using LLaMA Factory
<details><summary>Click to show</summary>
1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
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)
@@ -411,6 +412,8 @@ export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
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.
</details>
> [!TIP]
> If you have a project that should be incorporated, please contact via email or create a pull request.