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update to v0.9.1

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# Note: actually we do not support .env, just for reference
# api
API_HOST=0.0.0.0
API_PORT=8000
API_KEY=
API_MODEL_NAME=gpt-3.5-turbo
FASTAPI_ROOT_PATH=
# general
DISABLE_VERSION_CHECK=
FORCE_CHECK_IMPORTS=
LLAMAFACTORY_VERBOSITY=
USE_MODELSCOPE_HUB=
RECORD_VRAM=
# torchrun
FORCE_TORCHRUN=
MASTER_ADDR=
MASTER_PORT=
NNODES=
RANK=
NPROC_PER_NODE=
# wandb
WANDB_DISABLED=
WANDB_PROJECT=huggingface
WANDB_API_KEY=
# gradio ui
GRADIO_SHARE=False
GRADIO_SERVER_NAME=0.0.0.0
GRADIO_SERVER_PORT=
GRADIO_ROOT_PATH=
# setup
ENABLE_SHORT_CONSOLE=1
# reserved (do not use)
LLAMABOARD_ENABLED=
LLAMABOARD_WORKDIR=
Makefile
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.PHONY: quality style test
check_dirs := scripts src tests
check_dirs := scripts src tests setup.py
quality:
ruff check $(check_dirs)
......
README.md
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# <div align="center"><strong>LLaMA Factory</strong></div>
## 简介
![# LLaMA Factory](assets/logo.png)
LLaMA Factory是一个大语言模型训练和推理的框架，支持了魔搭社区（ModelScope）的模型和数据集资源。它允许用户通过内置的Web UI灵活定制100多个LLMs的微调，而无需编写代码。
[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers)
[![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE)
[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)
[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
[![Citation](https://img.shields.io/badge/citation-91-green)](#projects-using-llama-factory)
[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
[![Open in DSW](https://gallery.pai-ml.com/assets/open-in-dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
[![Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
[![Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
## 项目特色
[![GitHub Tread](https://trendshift.io/api/badge/repositories/4535)](https://trendshift.io/repositories/4535)
- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
- **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
- **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
- **先进算法**：GaLore、BAdam、Adam-mini、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
- **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
- **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
👋 Join our [WeChat](assets/wechat.jpg) or [NPU user group](assets/wechat_npu.jpg).
## 支持模型结构列表
\[ English | [中文](README_zh.md) \]
| 模型名 | 模型大小 | Template |
| ------------------------------------------------------------ | -------------------------------- | --------- |
| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [Gemma 2](https://huggingface.co/google) | 2B/9B | gemma |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [Llama 3/Llama 3.1](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
| [Qwen1.5 (Code/MoE)/Qwen2](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/32B/72B | qwen |
| [XVERSE](https://hf-mirror.com/xverse) | 7B/13B | xverse |
| [OLMo](https://hf-mirror.com/allenai) | 1B/7B | olmo |
**Fine-tuning a large language model can be easy as...**
持续更新中...
https://github.com/user-attachments/assets/7c96b465-9df7-45f4-8053-bf03e58386d3
> 对于所有“基座”（Base）模型，`template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Instruct/Chat）模型请务必使用**对应的模板**。
>
> 请务必在训练和推理时采用**完全一致**的模板。
Choose your path:
您也可以在 [template.py](src/llamafactory/data/template.py) 中添加自己的对话模板。
- **Colab**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
- **PAI-DSW**: [Llama3 Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)
- **Local machine**: Please refer to [usage](#getting-started)
- **Documentation (WIP)**: https://llamafactory.readthedocs.io/zh-cn/latest/
> **[!NOTE]**
>
> 1. `Baichuan 2` 需要卸载掉环境中的xformers库，当前仅支持Lora方式训练。
>
> 2. `XVERSE`在`tokenizer > 0.19`的版本下有兼容性问题报错`Exception: data did not match any variant of untagged enum PyPreTokenizerTypeWrappe`，需要使用[XVERSE-13B-256K-hf](https://huggingface.co/xverse/XVERSE-13B-256K/tree/main)中的`tokenizer_config.json.update`/`tokenizer.json.update`替换原有模型文件中的对应tokenizer文件，具体解决方法参考[xverse-ai/XVERSE-7B issues](https://github.com/xverse-ai/XVERSE-7B/issues/1)
> [!NOTE]
> Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.
## Table of Contents
- [Features](#features)
- [Benchmark](#benchmark)
- [Changelog](#changelog)
- [Supported Models](#supported-models)
- [Supported Training Approaches](#supported-training-approaches)
- [Provided Datasets](#provided-datasets)
- [Requirement](#requirement)
- [Getting Started](#getting-started)
- [Projects using LLaMA Factory](#projects-using-llama-factory)
- [License](#license)
- [Citation](#citation)
- [Acknowledgement](#acknowledgement)
## Features
- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
- **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
- **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
- **Practical tricks**: [FlashAttention-2](https://github.com/Dao-AILab/flash-attention), [Unsloth](https://github.com/unslothai/unsloth), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), RoPE scaling, NEFTune and rsLoRA.
- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
- **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
## Benchmark
Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning), LLaMA Factory's LoRA tuning offers up to **3.7 times faster** training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.
![benchmark](assets/benchmark.svg)
<details><summary>Definitions</summary>
- **Training Speed**: the number of training samples processed per second during the training. (bs=4, cutoff_len=1024)
- **Rouge Score**: Rouge-2 score on the development set of the [advertising text generation](https://aclanthology.org/D19-1321.pdf) task. (bs=4, cutoff_len=1024)
- **GPU Memory**: Peak GPU memory usage in 4-bit quantized training. (bs=1, cutoff_len=1024)
- We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA Factory's LoRA tuning.
</details>
## Changelog
[24/09/19] We support fine-tuning the **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** models.
[24/08/30] We support fine-tuning the **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** models. Thank [@simonJJJ](https://github.com/simonJJJ)'s PR.
[24/08/27] We support **[Liger Kernel](https://github.com/linkedin/Liger-Kernel)**. Try `enable_liger_kernel: true` for efficient training.
[24/08/09] We support **[Adam-mini](https://github.com/zyushun/Adam-mini)** optimizer. See [examples](examples/README.md) for usage. Thank [@relic-yuexi](https://github.com/relic-yuexi)'s PR.
<details><summary>Full Changelog</summary>
[24/07/04] We support [contamination-free packed training](https://github.com/MeetKai/functionary/tree/main/functionary/train/packing). Use `neat_packing: true` to activate it. Thank [@chuan298](https://github.com/chuan298)'s PR.
[24/06/16] We support **[PiSSA](https://arxiv.org/abs/2404.02948)** algorithm. See [examples](examples/README.md) for usage.
[24/06/07] We supported fine-tuning the **[Qwen2](https://qwenlm.github.io/blog/qwen2/)** and **[GLM-4](https://github.com/THUDM/GLM-4)** models.
[24/05/26] We supported **[SimPO](https://arxiv.org/abs/2405.14734)** algorithm for preference learning. See [examples](examples/README.md) for usage.
[24/05/20] We supported fine-tuning the **PaliGemma** series models. Note that the PaliGemma models are pre-trained models, you need to fine-tune them with `paligemma` template for chat completion.
[24/05/18] We supported **[KTO](https://arxiv.org/abs/2402.01306)** algorithm for preference learning. See [examples](examples/README.md) for usage.
[24/05/14] We supported training and inference on the Ascend NPU devices. Check [installation](#installation) section for details.
[24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See [examples](examples/README.md) for usage.
[24/04/22] We provided a **[Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)** for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details.
[24/04/21] We supported **[Mixture-of-Depths](https://arxiv.org/abs/2404.02258)** according to [AstraMindAI's implementation](https://github.com/astramind-ai/Mixture-of-depths). See [examples](examples/README.md) for usage.
[24/04/16] We supported **[BAdam](https://arxiv.org/abs/2404.02827)** optimizer. See [examples](examples/README.md) for usage.
[24/04/16] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s long-sequence training (Llama-2-7B-56k within 24GB). It achieves **117%** speed and **50%** memory compared with FlashAttention-2, more benchmarks can be found in [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison).
[24/03/31] We supported **[ORPO](https://arxiv.org/abs/2403.07691)**. See [examples](examples/README.md) for usage.
[24/03/21] Our paper "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" is available at arXiv!
[24/03/20] We supported **FSDP+QLoRA** that fine-tunes a 70B model on 2x24GB GPUs. See [examples](examples/README.md) for usage.
[24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. See [examples](examples/README.md) for usage.
[24/03/07] We supported **[GaLore](https://arxiv.org/abs/2403.03507)** optimizer. See [examples](examples/README.md) for usage.
[24/03/07] We integrated **[vLLM](https://github.com/vllm-project/vllm)** for faster and concurrent inference. Try `infer_backend: vllm` to enjoy **270%** inference speed.
[24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `use_dora: true` to activate DoRA training.
[24/02/15] We supported **block expansion** proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See [examples](examples/README.md) for usage.
[24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this [blog post](https://qwenlm.github.io/blog/qwen1.5/) for details.
[24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `dataset: glaive_toolcall_en`.
[23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `use_unsloth: true` argument to activate unsloth patch. It achieves **170%** speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details.
[23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement).
[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#download-from-modelscope-hub) for usage.
[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `neftune_noise_alpha: 5` argument to activate NEFTune.
[23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `shift_attn: true` argument to enable shift short attention.
[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [examples](examples/README.md) for usage.
[23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `flash_attn: fa2` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
[23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `rope_scaling: linear` argument in training and `rope_scaling: dynamic` argument at inference to extrapolate the position embeddings.
[23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [examples](examples/README.md) for usage.
[23/07/31] We supported **dataset streaming**. Try `streaming: true` and `max_steps: 10000` arguments to load your dataset in streaming mode.
[23/07/29] We released two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)) for details.
[23/07/18] We developed an **all-in-one Web UI** for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development.
[23/07/09] We released **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested.
[23/06/29] We provided a **reproducible example** of training a chat model using instruction-following datasets, see [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft) for details.
[23/06/22] We aligned the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**.
[23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). See [examples](examples/README.md) for usage.
</details>
## Supported Models
| Model | Model size | Template |
| ----------------------------------------------------------------- | -------------------------------- | ---------------- |
| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
| [InternLM2/InternLM2.5](https://huggingface.co/internlm) | 7B/20B | intern2 |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [Llama 3-3.2](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
| [LLaVA-NeXT](https://huggingface.co/llava-hf) | 7B/8B/13B/34B/72B/110B | llava_next |
| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf) | 7B/34B | llava_next_video |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B/4B | cpm/cpm3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | paligemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
| [Qwen (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
| [Qwen2-VL](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
> [!NOTE]
> For the "base" models, the `template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "instruct/chat" models.
>
> 3. `Qwen2`训练仅支持bf16格式，**fp16会出现loss为0，lr为0的问题**，参考[issues](https://github.com/hiyouga/LLaMA-Factory/issues/4848)
> Remember to use the **SAME** template in training and inference.
## 使用源码编译方式安装
### 环境准备
Please refer to [constants.py](src/llamafactory/extras/constants.py) for a full list of models we supported.
-v 路径、docker_name和imageID根据实际情况修改
You also can add a custom chat template to [template.py](src/llamafactory/data/template.py).
#### Docker（方法一）
## Supported Training Approaches
基于光源pytorch2.1.0基础镜像环境：镜像下载地址：[https://sourcefind.cn/#/image/dcu/pytorch](https://sourcefind.cn/#/image/dcu/pytorch)，根据pytorch2.1.0、python、dtk及系统下载对应的镜像版本。
| Approach | Full-tuning | Freeze-tuning | LoRA | QLoRA |
| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
| Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| Reward Modeling | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| PPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| DPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| KTO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| ORPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| SimPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
```bash
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
> [!TIP]
> The implementation details of PPO can be found in [this blog](https://newfacade.github.io/notes-on-reinforcement-learning/17-ppo-trl.html).
## Provided Datasets
<details><summary>Pre-training datasets</summary>
- [Wiki Demo (en)](data/wiki_demo.txt)
- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)
- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
- [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
- [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
</details>
<details><summary>Supervised fine-tuning datasets</summary>
- [Identity (en&zh)](data/identity.json)
- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)
- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
- [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
- [UltraChat (en)](https://github.com/thunlp/UltraChat)
- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
- [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
- [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa)
- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
- [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
- [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
- [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
- [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
- [Neo-sft (zh)](https://huggingface.co/datasets/m-a-p/neo_sft_phase2)
- [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)
- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)
- [Magpie-ultra-v0.1 (en)](https://huggingface.co/datasets/argilla/magpie-ultra-v0.1)
- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
- [Pokemon-gpt4o-captions (en&zh)](https://huggingface.co/datasets/jugg1024/pokemon-gpt4o-captions)
- [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
- [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
- [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)
- [OpenSchnabeltier (de)](https://huggingface.co/datasets/mayflowergmbh/openschnabeltier_de)
- [Evol Instruct (de)](https://huggingface.co/datasets/mayflowergmbh/evol-instruct_de)
- [Dolphin (de)](https://huggingface.co/datasets/mayflowergmbh/dolphin_de)
- [Booksum (de)](https://huggingface.co/datasets/mayflowergmbh/booksum_de)
- [Airoboros (de)](https://huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de)
- [Ultrachat (de)](https://huggingface.co/datasets/mayflowergmbh/ultra-chat_de)
</details>
<details><summary>Preference datasets</summary>
- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
- [RLHF-V (en)](https://huggingface.co/datasets/openbmb/RLHF-V-Dataset)
- [VLFeedback (en)](https://huggingface.co/datasets/Zhihui/VLFeedback)
- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
- [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)
</details>
Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
pip install vllm-0.5.0+das.opt1.3e2c63a.dtk2404.torch2.1.0-cp310-cp310-linux_x86_64.whl
pip install lmslim-0.0.1+das.dtk24041.torch2.1.0-cp310-cp310-linux_x86_64.whl
```bash
pip install --upgrade huggingface_hub
huggingface-cli login
```
#### Dockerfile（方法二）
## Requirement
```bash
cd docker
docker build --no-cache -t llama-factory:latest .
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
| Mandatory | Minimum | Recommend |
| ------------ | ------- | --------- |
| python | 3.8 | 3.11 |
| torch | 1.13.1 | 2.4.0 |
| transformers | 4.41.2 | 4.43.4 |
| datasets | 2.16.0 | 2.20.0 |
| accelerate | 0.30.1 | 0.32.0 |
| peft | 0.11.1 | 0.12.0 |
| trl | 0.8.6 | 0.9.6 |
pip install vllm-0.5.0+das.opt1.3e2c63a.dtk2404.torch2.1.0-cp310-cp310-linux_x86_64.whl
pip install lmslim-0.0.1+das.dtk24041.torch2.1.0-cp310-cp310-linux_x86_64.whl
```
| Optional | Minimum | Recommend |
| ------------ | ------- | --------- |
| CUDA | 11.6 | 12.2 |
| deepspeed | 0.10.0 | 0.14.0 |
| bitsandbytes | 0.39.0 | 0.43.1 |
| vllm | 0.4.3 | 0.5.0 |
| flash-attn | 2.3.0 | 2.6.3 |
### Hardware Requirement
\* *estimated*
#### Anaconda（方法三）
| Method | Bits | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B |
| ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
| Full | AMP | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
| Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB |
| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB |
| LoRA/GaLore/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB |
## Getting Started
### Installation
> [!IMPORTANT]
> Installation is mandatory.
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
```bash
DTK驱动: dtk24.04.1
python: 3.10
torch: 2.1.0
bitsandbytes: 0.42.0
vllm: 0.5.0
lmslim: 0.0.1
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[torch,metrics]"
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
### 源码编译安装
Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, quality
> [!TIP]
> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
> Use `pip install --no-deps -e .` to resolve package conflicts.
<details><summary>For Windows users</summary>
If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you need to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.2, please select the appropriate [release version](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels) based on your CUDA version.
```bash
git clone http://developer.hpccube.com/codes/OpenDAS/llama-factory.git
cd /your_code_path/llama_factory
pip install -e ".[torch,metrics]"
pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
```
To enable FlashAttention-2 on the Windows platform, you need to install the precompiled `flash-attn` library, which supports CUDA 12.1 to 12.2. Please download the corresponding version from [flash-attention](https://github.com/bdashore3/flash-attention/releases) based on your requirements.
</details>
# pdsh安装，deepspeed多机需要，若已安装，可忽略。
# 安装需要root权限
cd ../
#下载解压
wget https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/pdsh/pdsh-2.29.tar.bz2 && tar -xf pdsh-2.29.tar.bz2
#编译安装
cd pdsh-2.29 && ./configure --with-ssh --enable-static-modules --prefix=/usr/local && make && make install
#测试
pdsh -V
<details><summary>For Ascend NPU users</summary>
To install LLaMA Factory on Ascend NPU devices, please specify extra dependencies: `pip install -e ".[torch-npu,metrics]"`. Additionally, you need to install the **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**. Please follow the [installation tutorial](https://www.hiascend.com/document/detail/en/CANNCommunityEdition/600alphaX/softwareinstall/instg/atlasdeploy_03_0031.html) or use the following commands:
```bash
# replace the url according to your CANN version and devices
# install CANN Toolkit
wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run
bash Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run --install
# install CANN Kernels
wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run
bash Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run --install
# set env variables
source /usr/local/Ascend/ascend-toolkit/set_env.sh
```
## 如何使用
### 数据准备
| Requirement | Minimum | Recommend |
| ------------ | ------- | ----------- |
| CANN | 8.0.RC1 | 8.0.RC1 |
| torch | 2.1.0 | 2.1.0 |
| torch-npu | 2.1.0 | 2.1.0.post3 |
| deepspeed | 0.13.2 | 0.13.2 |
Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.
If you cannot infer model on NPU devices, try setting `do_sample: false` in the configurations.
Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
</details>
关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope 上的数据集或加载本地数据集。
### Data Preparation
Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk.
> [!NOTE]
> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。
> Please update `data/dataset_info.json` to use your custom dataset.
### 快速开始
### Quickstart
下面三行命令分别对 Llama3-8B-Instruct 模型进行 LoRA **微调**、**推理**和**合并**。根据实际情况修改参数，如`model_name_or_path`/`dataset`/`template`等。
Use the following 3 commands to run LoRA **fine-tuning**, **inference** and **merging** of the Llama3-8B-Instruct model, respectively.
```bash
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
......@@ -121,12 +430,297 @@ llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
```
高级用法请参考 [examples/README_zh.md](examples/README_zh.md)（包括多 GPU 微调）。
See [examples/README.md](examples/README.md) for advanced usage (including distributed training).
> [!TIP]
> Use `llamafactory-cli help` to show help information.
### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))
```bash
llamafactory-cli webui
```
### Build Docker
For CUDA users:
```bash
cd docker/docker-cuda/
docker compose up -d
docker compose exec llamafactory bash
```
For Ascend NPU users:
```bash
cd docker/docker-npu/
docker compose up -d
docker compose exec llamafactory bash
```
For AMD ROCm users:
```bash
cd docker/docker-rocm/
docker compose up -d
docker compose exec llamafactory bash
```
<details><summary>Build without Docker Compose</summary>
For CUDA users:
```bash
docker build -f ./docker/docker-cuda/Dockerfile \
--build-arg INSTALL_BNB=false \
--build-arg INSTALL_VLLM=false \
--build-arg INSTALL_DEEPSPEED=false \
--build-arg INSTALL_FLASHATTN=false \
--build-arg PIP_INDEX=https://pypi.org/simple \
-t llamafactory:latest .
docker run -dit --gpus=all \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./data:/app/data \
-v ./output:/app/output \
-p 7860:7860 \
-p 8000:8000 \
--shm-size 16G \
--name llamafactory \
llamafactory:latest
docker exec -it llamafactory bash
```
For Ascend NPU users:
```bash
# Choose docker image upon your environment
docker build -f ./docker/docker-npu/Dockerfile \
--build-arg INSTALL_DEEPSPEED=false \
--build-arg PIP_INDEX=https://pypi.org/simple \
-t llamafactory:latest .
# Change `device` upon your resources
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./data:/app/data \
-v ./output:/app/output \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-p 7860:7860 \
-p 8000:8000 \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
--shm-size 16G \
--name llamafactory \
llamafactory:latest
docker exec -it llamafactory bash
```
For AMD ROCm users:
```bash
docker build -f ./docker/docker-rocm/Dockerfile \
--build-arg INSTALL_BNB=false \
--build-arg INSTALL_VLLM=false \
--build-arg INSTALL_DEEPSPEED=false \
--build-arg INSTALL_FLASHATTN=false \
--build-arg PIP_INDEX=https://pypi.org/simple \
-t llamafactory:latest .
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./data:/app/data \
-v ./output:/app/output \
-v ./saves:/app/saves \
-p 7860:7860 \
-p 8000:8000 \
--device /dev/kfd \
--device /dev/dri \
--shm-size 16G \
--name llamafactory \
llamafactory:latest
docker exec -it llamafactory bash
```
</details>
<details><summary>Details about volume</summary>
- `hf_cache`: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
- `ms_cache`: Similar to Hugging Face cache but for ModelScope users.
- `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>
### Deploy with OpenAI-style API and vLLM
```bash
API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
```
> [!TIP]
> 使用 `llamafactory-cli help` 显示帮助信息。
> Visit [this page](https://platform.openai.com/docs/api-reference/chat/create) for API document.
### Download from ModelScope Hub
If you have trouble with downloading models and datasets from Hugging Face, you can use ModelScope.
```bash
export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
```
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., `LLM-Research/Meta-Llama-3-8B-Instruct`.
### Use W&B Logger
To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments to yaml files.
```yaml
report_to: wandb
run_name: test_run # optional
```
Set `WANDB_API_KEY` to [your key](https://wandb.ai/authorize) when launching training tasks to log in with your W&B account.
## Projects using LLaMA Factory
If you have a project that should be incorporated, please contact via email or create a pull request.
<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)
1. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [[arxiv]](https://arxiv.org/abs/2311.07816)
1. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [[arxiv]](https://arxiv.org/abs/2312.15710)
1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. KDD 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
1. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2402.05904)
1. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [[arxiv]](https://arxiv.org/abs/2402.07625)
1. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11176)
1. Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [[arxiv]](https://arxiv.org/abs/2402.11187)
1. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [[arxiv]](https://arxiv.org/abs/2402.11746)
1. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11801)
1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2402.11809)
1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819)
1. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [[arxiv]](https://arxiv.org/abs/2402.12204)
1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714)
1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)
1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)
1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)
1. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2403.09073)
1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)
1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. COLING 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)
1. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2404.00604)
1. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.02827)
1. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2404.04167)
1. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. ICML 2024. [[arxiv]](https://arxiv.org/abs/2404.04316)
1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
1. Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.09836)
1. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.11581)
1. Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [[arxiv]](https://arxiv.org/abs/2404.14215)
1. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2404.16621)
1. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2404.17140)
1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
1. Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [[arxiv]](https://arxiv.org/abs/2405.04760)
1. Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [[arxiv]](https://arxiv.org/abs/2405.05378)
1. Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [[arxiv]](https://arxiv.org/abs/2405.09055)
1. Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [[arxiv]](https://arxiv.org/abs/2405.12739)
1. Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2405.13816)
1. Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2405.20215)
1. Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [[paper]](https://aclanthology.org/2024.lt4hala-1.30)
1. Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2406.00380)
1. Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [[arxiv]](https://arxiv.org/abs/2406.02106)
1. Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [[arxiv]](https://arxiv.org/abs/2406.03136)
1. Ge et al. Time Sensitive Knowledge Editing through Efficient Finetuning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2406.04496)
1. Tan et al. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions. 2024. [[arxiv]](https://arxiv.org/abs/2406.05688)
1. Song et al. Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters. 2024. [[arxiv]](https://arxiv.org/abs/2406.05955)
1. Gu et al. RWKV-CLIP: A Robust Vision-Language Representation Learner. 2024. [[arxiv]](https://arxiv.org/abs/2406.06973)
1. Chen et al. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. 2024. [[arxiv]](https://arxiv.org/abs/2406.07115)
1. Zhu et al. Are Large Language Models Good Statisticians?. 2024. [[arxiv]](https://arxiv.org/abs/2406.07815)
1. Li et al. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2406.10099)
1. Ding et al. IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce. 2024. [[arxiv]](https://arxiv.org/abs/2406.10173)
1. He et al. COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities. 2024. [[arxiv]](https://arxiv.org/abs/2406.12074)
1. Lin et al. FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. 2024. [[arxiv]](https://arxiv.org/abs/2406.14408)
1. Treutlein et al. Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. 2024. [[arxiv]](https://arxiv.org/abs/2406.14546)
1. Feng et al. SS-Bench: A Benchmark for Social Story Generation and Evaluation. 2024. [[arxiv]](https://arxiv.org/abs/2406.15695)
1. Feng et al. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. 2024. [[arxiv]](https://arxiv.org/abs/2406.17233)
1. Liu et al. Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals. 2024. [[arxiv]](https://arxiv.org/abs/2406.18069)
1. Iyer et al. Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh's Submission to AmericasNLP 2024 Translation Task. AmericasNLP 2024. [[paper]](https://aclanthology.org/2024.americasnlp-1.25)
1. Li et al. Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring. 2024. [[arxiv]](https://arxiv.org/abs/2406.19949)
1. Yang et al. Financial Knowledge Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2407.00365)
1. Lin et al. DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging. 2024. [[arxiv]](https://arxiv.org/abs/2407.01470)
1. Bako et al. Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualization. 2024. [[arxiv]](https://arxiv.org/abs/2407.06129)
1. Huang et al. RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization. 2024. [[arxiv]](https://arxiv.org/abs/2407.08044)
1. Jiang et al. LLM-Collaboration on Automatic Science Journalism for the General Audience. 2024. [[arxiv]](https://arxiv.org/abs/2407.09756)
1. Inouye et al. Applied Auto-tuning on LoRA Hyperparameters. 2024. [[paper]](https://scholarcommons.scu.edu/cseng_senior/272/)
1. Qi et al. Research on Tibetan Tourism Viewpoints information generation system based on LLM. 2024. [[arxiv]](https://arxiv.org/abs/2407.13561)
1. Xu et al. Course-Correction: Safety Alignment Using Synthetic Preferences. 2024. [[arxiv]](https://arxiv.org/abs/2407.16637)
1. Sun et al. LAMBDA: A Large Model Based Data Agent. 2024. [[arxiv]](https://arxiv.org/abs/2407.17535)
1. Zhu et al. CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2407.19705)
1. Yu et al. Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2408.00137)
1. Xie et al. The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning. IALP 2024. [[paper]](https://www.asianlp.sg/conferences/ialp2024/proceedings/papers/IALP2024_P055.pdf)
1. Liu et al. Instruct-Code-Llama: Improving Capabilities of Language Model in Competition Level Code Generation by Online Judge Feedback. ICIC 2024. [[paper]](https://link.springer.com/chapter/10.1007/978-981-97-5669-8_11)
1. Wang et al. Cybernetic Sentinels: Unveiling the Impact of Safety Data Selection on Model Security in Supervised Fine-Tuning. ICIC 2024. [[paper]](https://link.springer.com/chapter/10.1007/978-981-97-5669-8_23)
1. Xia et al. Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2408.04693)
1. Zeng et al. Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2408.04168)
1. Xia et al. Using Pre-trained Language Model for Accurate ESG Prediction. FinNLP 2024. [[paper]](https://aclanthology.org/2024.finnlp-2.1/)
1. Liang et al. I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm. 2024. [[arxiv]](https://arxiv.org/abs/2408.08072)
1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
1. **[Sunsimiao](https://github.com/X-D-Lab/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
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.
1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**: A large language model specialized in generate metadata for stable diffusion. [[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
1. **[Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med)**: A multimodal large language model specialized in Chinese medical domain, based on LLaVA-1.5-7B.
1. **[AutoRE](https://github.com/THUDM/AutoRE)**: A document-level relation extraction system based on large language models.
1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**: SDKs for fine-tuning LLMs on Windows PC for NVIDIA RTX.
1. **[LazyLLM](https://github.com/LazyAGI/LazyLLM)**: An easy and lazy way for building multi-agent LLMs applications and supports model fine-tuning via LLaMA Factory.
</details>
## License
This repository is licensed under the [Apache-2.0 License](LICENSE).
Please follow the model licenses to use the corresponding model weights: [Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## Citation
If this work is helpful, please kindly cite as:
```bibtex
@inproceedings{zheng2024llamafactory,
title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Zhangchi Feng and Yongqiang Ma},
booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
address={Bangkok, Thailand},
publisher={Association for Computational Linguistics},
year={2024},
url={http://arxiv.org/abs/2403.13372}
}
```
## Acknowledgement
This repo benefits from [PEFT](https://github.com/huggingface/peft), [TRL](https://github.com/huggingface/trl), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works.
## 参考资料
## Star History
- [README_zh](README_zh.md)
- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date)
README_zh.md
View file @ 27a7ad86
......@@ -26,11 +26,14 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
选择你的打开方式：
- **Colab**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
- **PAI-DSW**：https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
- **PAI-DSW**：[Llama3 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)
- **本地机器**：请见[如何使用](#如何使用)
- **入门教程**：https://zhuanlan.zhihu.com/p/695287607
- **框架文档**：https://llamafactory.readthedocs.io/zh-cn/latest/
> [!NOTE]
> 除上述链接以外的其他网站均为未经许可的第三方网站，请小心甄别。
## 目录
- [项目特色](#项目特色)
......@@ -48,11 +51,11 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
## 项目特色
- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Qwen2-VL、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
- **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
- **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
- **先进算法**：GaLore、BAdam、Adam-mini、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
- **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
- **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[Adam-mini](https://github.com/zyushun/Adam-mini)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
- **实用技巧**：[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)、[Unsloth](https://github.com/unslothai/unsloth)、[Liger Kernel](https://github.com/linkedin/Liger-Kernel)、RoPE scaling、NEFTune 和 rsLoRA。
- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
- **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
......@@ -73,19 +76,25 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
## 更新日志
[24/08/09] 我们支持了 **[Adam-mini](https://arxiv.org/abs/2406.16793)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。感谢 [@relic-yuexi](https://github.com/relic-yuexi) 的 PR。
[24/09/19] 我们支持了 **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** 模型的微调。
[24/07/04] 我们支持了[无污染打包训练](https://github.com/MeetKai/functionary/tree/main/functionary/train/packing)。请使用 `neat_packing: true` 参数。感谢 [@chuan298](https://github.com/chuan298) 的 PR。
[24/08/30] 我们支持了 **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** 模型的微调。感谢 [@simonJJJ](https://github.com/simonJJJ) 的 PR。
[24/06/16] 我们支持了 **[PiSSA](https://arxiv.org/abs/2404.02948)** 算法。详细用法请参照 [examples](examples/README_zh.md)。
[24/08/27] 我们支持了 **[Liger Kernel](https://github.com/linkedin/Liger-Kernel)**。请使用 `enable_liger_kernel: true` 来加速训练。
[24/08/09] 我们支持了 **[Adam-mini](https://github.com/zyushun/Adam-mini)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。感谢 [@relic-yuexi](https://github.com/relic-yuexi) 的 PR。
<details><summary>展开日志</summary>
[24/07/04] 我们支持了[无污染打包训练](https://github.com/MeetKai/functionary/tree/main/functionary/train/packing)。请使用 `neat_packing: true` 参数。感谢 [@chuan298](https://github.com/chuan298) 的 PR。
[24/06/16] 我们支持了 **[PiSSA](https://arxiv.org/abs/2404.02948)** 算法。详细用法请参照 [examples](examples/README_zh.md)。
[24/06/07] 我们支持了 **[Qwen2](https://qwenlm.github.io/blog/qwen2/)** 和 **[GLM-4](https://github.com/THUDM/GLM-4)** 模型的微调。
[24/05/26] 我们支持了 **[SimPO](https://arxiv.org/abs/2405.14734)** 偏好对齐算法。详细用法请参照 [examples](examples/README_zh.md)。
[24/05/20] 我们支持了 **PaliGemma** 系列模型的微调。注意 PaliGemma 是预训练模型，你需要使用 `gemma` 模板进行微调使其获得对话能力。
[24/05/20] 我们支持了 **PaliGemma** 系列模型的微调。注意 PaliGemma 是预训练模型，你需要使用 `paligemma` 模板进行微调使其获得对话能力。
[24/05/18] 我们支持了 **[KTO](https://arxiv.org/abs/2402.01306)** 偏好对齐算法。详细用法请参照 [examples](examples/README_zh.md)。
......@@ -158,7 +167,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
## 模型
| 模型名 | 模型大小 | Template |
| ----------------------------------------------------------------- | -------------------------------- | --------- |
| ----------------------------------------------------------------- | -------------------------------- | ---------------- |
| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
......@@ -170,18 +179,21 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
| [InternLM2/InternLM2.5](https://huggingface.co/internlm) | 7B/20B | intern2 |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [Llama 3/Llama 3.1](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | vicuna |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B | cpm |
| [Llama 3-3.2](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
| [LLaVA-NeXT](https://huggingface.co/llava-hf) | 7B/8B/13B/34B/72B/110B | llava_next |
| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf) | 7B/34B | llava_next_video |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B/4B | cpm/cpm3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | gemma |
| [PaliGemma](https://huggingface.co/google) | 3B | paligemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
| [Qwen/Qwen1.5/Qwen2 (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen |
| [Qwen (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
| [Qwen2-VL](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5](https://huggingface.co/01-ai) | 6B/9B/34B | yi |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
......@@ -271,6 +283,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)
- [Magpie-ultra-v0.1 (en)](https://huggingface.co/datasets/argilla/magpie-ultra-v0.1)
- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
- [Pokemon-gpt4o-captions (en&zh)](https://huggingface.co/datasets/jugg1024/pokemon-gpt4o-captions)
- [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
- [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
- [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)
......@@ -287,6 +300,8 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
- [RLHF-V (en)](https://huggingface.co/datasets/openbmb/RLHF-V-Dataset)
- [VLFeedback (en)](https://huggingface.co/datasets/Zhihui/VLFeedback)
- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
......@@ -349,7 +364,7 @@ cd LLaMA-Factory
pip install -e ".[torch,metrics]"
```
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、adam-mini、qwen、modelscope、quality
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、adam-mini、qwen、modelscope、quality
> [!TIP]
> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
......
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data/README.md
View file @ 27a7ad86
......@@ -23,6 +23,7 @@ Currently we support datasets in **alpaca** and **sharegpt** format.
"system": "the column name in the dataset containing the system prompts. (default: None)",
"tools": "the column name in the dataset containing the tool description. (default: None)",
"images": "the column name in the dataset containing the image inputs. (default: None)",
"videos": "the column name in the dataset containing the videos inputs. (default: None)",
"chosen": "the column name in the dataset containing the chosen answers. (default: None)",
"rejected": "the column name in the dataset containing the rejected answers. (default: None)",
"kto_tag": "the column name in the dataset containing the kto tags. (default: None)"
......@@ -107,7 +108,7 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
### Preference Dataset
Preference datasets are used for reward modeling, DPO training and ORPO training.
Preference datasets are used for reward modeling, DPO training, ORPO and SimPO training.
It requires a better response in `chosen` column and a worse response in `rejected` column.
......@@ -139,67 +140,15 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
### KTO Dataset
- [Example dataset](kto_en_demo.json)
KTO datasets require a extra `kto_tag` column containing the boolean human feedback.
```json
[
{
"instruction": "human instruction (required)",
"input": "human input (optional)",
"output": "model response (required)",
"kto_tag": "human feedback [true/false] (required)"
}
]
```
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"columns": {
"prompt": "instruction",
"query": "input",
"response": "output",
"kto_tag": "kto_tag"
}
}
```
### Multimodal Dataset
An additional column `kto_tag` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
- [Example dataset](mllm_demo.json)
### Multimodal Image Dataset
Multimodal datasets require a `images` column containing the paths to the input images. Currently we only support one image.
```json
[
{
"instruction": "human instruction (required)",
"input": "human input (optional)",
"output": "model response (required)",
"images": [
"image path (required)"
]
}
]
```
An additional column `images` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
### Multimodal Video Dataset
```json
"dataset_name": {
"file_name": "data.json",
"columns": {
"prompt": "instruction",
"query": "input",
"response": "output",
"images": "images"
}
}
```
An additional column `videos` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
## Sharegpt Format
......@@ -252,6 +201,10 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
}
```
### Pre-training Dataset
Not yet supported, please use the [alpaca](#alpaca-format) format.
### Preference Dataset
- [Example dataset](dpo_en_demo.json)
......@@ -302,6 +255,125 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
}
```
### KTO Dataset
- [Example dataset](kto_en_demo.json)
KTO datasets require a extra `kto_tag` column containing the boolean human feedback.
```json
[
{
"conversations": [
{
"from": "human",
"value": "human instruction"
},
{
"from": "gpt",
"value": "model response"
}
],
"kto_tag": "human feedback [true/false] (required)"
}
]
```
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"kto_tag": "kto_tag"
}
}
```
### Multimodal Image Dataset
- [Example dataset](mllm_demo.json)
Multimodal image datasets require a `images` column containing the paths to the input images.
The number of images should be identical to the `<image>` tokens in the conversations.
```json
[
{
"conversations": [
{
"from": "human",
"value": "<image>human instruction"
},
{
"from": "gpt",
"value": "model response"
}
],
"images": [
"image path (required)"
]
}
]
```
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"images": "images"
}
}
```
### Multimodal Video Dataset
- [Example dataset](mllm_video_demo.json)
Multimodal video datasets require a `videos` column containing the paths to the input videos.
The number of videos should be identical to the `<video>` tokens in the conversations.
```json
[
{
"conversations": [
{
"from": "human",
"value": "<video>human instruction"
},
{
"from": "gpt",
"value": "model response"
}
],
"videos": [
"video path (required)"
]
}
]
```
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"videos": "videos"
}
}
```
### OpenAI Format
The openai format is simply a special case of the sharegpt format, where the first message may be a system prompt.
......@@ -345,7 +417,3 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
}
}
```
The KTO datasets and multimodal datasets in sharegpt format are similar to the alpaca format.
Pre-training datasets are **incompatible** with the sharegpt format.
data/README_zh.md
View file @ 27a7ad86
......@@ -23,6 +23,7 @@
"system": "数据集代表系统提示的表头名称（默认：None）",
"tools": "数据集代表工具描述的表头名称（默认：None）",
"images": "数据集代表图像输入的表头名称（默认：None）",
"videos": "数据集代表视频输入的表头名称（默认：None）",
"chosen": "数据集代表更优回答的表头名称（默认：None）",
"rejected": "数据集代表更差回答的表头名称（默认：None）",
"kto_tag": "数据集代表 KTO 标签的表头名称（默认：None）"
......@@ -107,7 +108,7 @@
### 偏好数据集
偏好数据集用于奖励模型训练、DPO 训练和 ORPO 训练。
偏好数据集用于奖励模型训练、DPO 训练、ORPO 训练和 SimPO 训练。
它需要在 `chosen` 列中提供更优的回答，并在 `rejected` 列中提供更差的回答。
......@@ -139,67 +140,15 @@
### KTO 数据集
- [样例数据集](kto_en_demo.json)
KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人类反馈。
```json
[
{
"instruction": "人类指令（必填）",
"input": "人类输入（选填）",
"output": "模型回答（必填）",
"kto_tag": "人类反馈 [true/false]（必填）"
}
]
```
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"columns": {
"prompt": "instruction",
"query": "input",
"response": "output",
"kto_tag": "kto_tag"
}
}
```
### 多模态数据集
KTO 数据集需要提供额外的 `kto_tag` 列。详情请参阅 [sharegpt](#sharegpt-格式)。
- [样例数据集](mllm_demo.json)
### 多模态图像数据集
多模态数据集需要额外添加一个 `images` 列，包含输入图像的路径。目前我们仅支持单张图像输入。
```json
[
{
"instruction": "人类指令（必填）",
"input": "人类输入（选填）",
"output": "模型回答（必填）",
"images": [
"图像路径（必填）"
]
}
]
```
多模态图像数据集需要提供额外的 `images` 列。详情请参阅 [sharegpt](#sharegpt-格式)。
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
### 多模态视频数据集
```json
"数据集名称": {
"file_name": "data.json",
"columns": {
"prompt": "instruction",
"query": "input",
"response": "output",
"images": "images"
}
}
```
多模态视频数据集需要提供额外的 `videos` 列。详情请参阅 [sharegpt](#sharegpt-格式)。
## Sharegpt 格式
......@@ -252,6 +201,10 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
}
```
### 预训练数据集
尚不支持，请使用 [alpaca](#alpaca-格式) 格式。
### 偏好数据集
- [样例数据集](dpo_zh_demo.json)
......@@ -302,6 +255,125 @@ Sharegpt 格式的偏好数据集同样需要在 `chosen` 列中提供更优的
}
```
### KTO 数据集
- [样例数据集](kto_en_demo.json)
KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人类反馈。
```json
[
{
"conversations": [
{
"from": "human",
"value": "人类指令"
},
{
"from": "gpt",
"value": "模型回答"
}
],
"kto_tag": "人类反馈 [true/false]（必填）"
}
]
```
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"kto_tag": "kto_tag"
}
}
```
### 多模态图像数据集
- [样例数据集](mllm_demo.json)
多模态图像数据集需要额外添加一个 `images` 列，包含输入图像的路径。
注意图片的数量必须与文本中所有 `<image>` 标记的数量严格一致。
```json
[
{
"conversations": [
{
"from": "human",
"value": "<image>人类指令"
},
{
"from": "gpt",
"value": "模型回答"
}
],
"images": [
"图像路径（必填）"
]
}
]
```
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"images": "images"
}
}
```
### 多模态视频数据集
- [样例数据集](mllm_video_demo.json)
多模态视频数据集需要额外添加一个 `videos` 列，包含输入视频的路径。
注意视频的数量必须与文本中所有 `<video>` 标记的数量严格一致。
```json
[
{
"conversations": [
{
"from": "human",
"value": "<video>人类指令"
},
{
"from": "gpt",
"value": "模型回答"
}
],
"videos": [
"视频路径（必填）"
]
}
]
```
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"videos": "videos"
}
}
```
### OpenAI 格式
OpenAI 格式仅仅是 sharegpt 格式的一种特殊情况，其中第一条消息可能是系统提示词。
......@@ -345,7 +417,3 @@ OpenAI 格式仅仅是 sharegpt 格式的一种特殊情况，其中第一条消
}
}
```
Sharegpt 格式中的 KTO 数据集和多模态数据集与 alpaca 格式的类似。
预训练数据集**不支持** sharegpt 格式。
data/dataset_info.json
View file @ 27a7ad86
......@@ -38,6 +38,20 @@
"assistant_tag": "assistant"
}
},
"mllm_video_demo": {
"file_name": "mllm_video_demo.json",
"formatting": "sharegpt",
"columns": {
"messages": "messages",
"videos": "videos"
},
"tags": {
"role_tag": "role",
"content_tag": "content",
"user_tag": "user",
"assistant_tag": "assistant"
}
},
"alpaca_en": {
"hf_hub_url": "llamafactory/alpaca_en",
"ms_hub_url": "llamafactory/alpaca_en"
......@@ -340,6 +354,14 @@
"assistant_tag": "assistant"
}
},
"pokemon_cap": {
"hf_hub_url": "llamafactory/pokemon-gpt4o-captions",
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"images": "images"
}
},
"mllm_pt_demo": {
"hf_hub_url": "BUAADreamer/mllm_pt_demo",
"formatting": "sharegpt",
......@@ -433,6 +455,28 @@
"rejected": "rejected"
}
},
"rlhf_v": {
"hf_hub_url": "llamafactory/RLHF-V",
"ranking": true,
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"chosen": "chosen",
"rejected": "rejected",
"images": "images"
}
},
"vlfeedback": {
"hf_hub_url": "Zhihui/VLFeedback",
"ranking": true,
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"chosen": "chosen",
"rejected": "rejected",
"images": "images"
}
},
"orca_pairs": {
"hf_hub_url": "Intel/orca_dpo_pairs",
"ranking": true,
......
data/mllm_demo.json
View file @ 27a7ad86
......@@ -2,7 +2,7 @@
{
"messages": [
{
"content": "Who are they?",
"content": "<image>Who are they?",
"role": "user"
},
{
......@@ -25,7 +25,7 @@
{
"messages": [
{
"content": "Who is he?",
"content": "<image>Who is he?",
"role": "user"
},
{
......@@ -48,7 +48,7 @@
{
"messages": [
{
"content": "Please describe this image",
"content": "<image>Please describe this image",
"role": "user"
},
{
......@@ -71,7 +71,7 @@
{
"messages": [
{
"content": "他们是谁？",
"content": "<image>他们是谁？",
"role": "user"
},
{
......@@ -94,7 +94,7 @@
{
"messages": [
{
"content": "他是谁？",
"content": "<image>他是谁？",
"role": "user"
},
{
......@@ -117,7 +117,7 @@
{
"messages": [
{
"content": "请描述这张图片",
"content": "<image>请描述这张图片",
"role": "user"
},
{
......
data/mllm_video_demo.json 0 → 100644
View file @ 27a7ad86
[
{
"messages": [
{
"content": "<video>Why is this video funny?",
"role": "user"
},
{
"content": "Because a baby is reading, and he is so cute!",
"role": "assistant"
}
],
"videos": [
"mllm_demo_data/1.mp4"
]
},
{
"messages": [
{
"content": "<video>What is she doing?",
"role": "user"
},
{
"content": "She is cooking.",
"role": "assistant"
}
],
"videos": [
"mllm_demo_data/2.avi"
]
},
{
"messages": [
{
"content": "<video>What's in the video?",
"role": "user"
},
{
"content": "A baby is playing in the living room.",
"role": "assistant"
}
],
"videos": [
"mllm_demo_data/3.mp4"
]
}
]
\ No newline at end of file
docker/docker-cuda/Dockerfile 0 → 100644
View file @ 27a7ad86
# Use the NVIDIA official image with PyTorch 2.3.0
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-02.html
FROM nvcr.io/nvidia/pytorch:24.02-py3
# Define environments
ENV MAX_JOBS=4
ENV FLASH_ATTENTION_FORCE_BUILD=TRUE
ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
# Define installation arguments
ARG INSTALL_BNB=false
ARG INSTALL_VLLM=false
ARG INSTALL_DEEPSPEED=false
ARG INSTALL_FLASHATTN=false
ARG INSTALL_LIGER_KERNEL=false
ARG INSTALL_HQQ=false
ARG INSTALL_EETQ=false
ARG PIP_INDEX=https://pypi.org/simple
# Set the working directory
WORKDIR /app
# Install the requirements
COPY requirements.txt /app
RUN pip config set global.index-url "$PIP_INDEX" && \
pip config set global.extra-index-url "$PIP_INDEX" && \
python -m pip install --upgrade pip && \
python -m pip install -r requirements.txt
# Copy the rest of the application into the image
COPY . /app
# Install the LLaMA Factory
RUN EXTRA_PACKAGES="metrics"; \
if [ "$INSTALL_BNB" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},bitsandbytes"; \
fi; \
if [ "$INSTALL_VLLM" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},vllm"; \
fi; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
fi; \
if [ "$INSTALL_HQQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
fi; \
if [ "$INSTALL_EETQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},eetq"; \
fi; \
pip install -e ".[$EXTRA_PACKAGES]"
# Rebuild flash attention
RUN pip uninstall -y transformer-engine flash-attn && \
if [ "$INSTALL_FLASHATTN" == "true" ]; then \
pip uninstall -y ninja && pip install ninja && \
pip install --no-cache-dir flash-attn --no-build-isolation; \
fi
# Set up volumes
VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]
# Expose port 7860 for the LLaMA Board
ENV GRADIO_SERVER_PORT 7860
EXPOSE 7860
# Expose port 8000 for the API service
ENV API_PORT 8000
EXPOSE 8000
docker/docker-cuda/docker-compose.yml 0 → 100644
View file @ 27a7ad86
services:
llamafactory:
build:
dockerfile: ./docker/docker-cuda/Dockerfile
context: ../..
args:
INSTALL_BNB: false
INSTALL_VLLM: false
INSTALL_DEEPSPEED: false
INSTALL_FLASHATTN: false
INSTALL_LIGER_KERNEL: false
INSTALL_HQQ: false
INSTALL_EETQ: false
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../data:/app/data
- ../../output:/app/output
ports:
- "7860:7860"
- "8000:8000"
ipc: host
tty: true
stdin_open: true
command: bash
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: "all"
capabilities: [gpu]
restart: unless-stopped
docker/docker-npu/Dockerfile 0 → 100644
View file @ 27a7ad86
# Use the Ubuntu 22.04 image with CANN 8.0.rc1
# More versions can be found at https://hub.docker.com/r/ascendai/cann/tags
# FROM ascendai/cann:8.0.rc1-910-ubuntu22.04-py3.8
FROM ascendai/cann:8.0.rc1-910b-ubuntu22.04-py3.8
# FROM ascendai/cann:8.0.rc1-910-openeuler22.03-py3.8
# FROM ascendai/cann:8.0.rc1-910b-openeuler22.03-py3.8
# Define environments
ENV DEBIAN_FRONTEND=noninteractive
# Define installation arguments
ARG INSTALL_DEEPSPEED=false
ARG PIP_INDEX=https://pypi.org/simple
ARG TORCH_INDEX=https://download.pytorch.org/whl/cpu
# Set the working directory
WORKDIR /app
# Install the requirements
COPY requirements.txt /app
RUN pip config set global.index-url "$PIP_INDEX" && \
pip config set global.extra-index-url "$TORCH_INDEX" && \
python -m pip install --upgrade pip && \
python -m pip install -r requirements.txt
# Copy the rest of the application into the image
COPY . /app
# Install the LLaMA Factory
RUN EXTRA_PACKAGES="torch-npu,metrics"; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
pip install -e ".[$EXTRA_PACKAGES]"
# Set up volumes
VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]
# Expose port 7860 for the LLaMA Board
ENV GRADIO_SERVER_PORT 7860
EXPOSE 7860
# Expose port 8000 for the API service
ENV API_PORT 8000
EXPOSE 8000
docker/docker-npu/docker-compose.yml 0 → 100644
View file @ 27a7ad86
services:
llamafactory:
build:
dockerfile: ./docker/docker-npu/Dockerfile
context: ../..
args:
INSTALL_DEEPSPEED: false
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../data:/app/data
- ../../output:/app/output
- /usr/local/dcmi:/usr/local/dcmi
- /usr/local/bin/npu-smi:/usr/local/bin/npu-smi
- /usr/local/Ascend/driver:/usr/local/Ascend/driver
- /etc/ascend_install.info:/etc/ascend_install.info
ports:
- "7860:7860"
- "8000:8000"
ipc: host
tty: true
stdin_open: true
command: bash
devices:
- /dev/davinci0
- /dev/davinci_manager
- /dev/devmm_svm
- /dev/hisi_hdc
restart: unless-stopped
docker/docker-rocm/Dockerfile 0 → 100644
View file @ 27a7ad86
FROM hardandheavy/transformers-rocm:2.2.0
# Define environments
ENV MAX_JOBS=4
ENV FLASH_ATTENTION_FORCE_BUILD=TRUE
ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
# Define installation arguments
ARG INSTALL_BNB=false
ARG INSTALL_VLLM=false
ARG INSTALL_DEEPSPEED=false
ARG INSTALL_FLASHATTN=false
ARG INSTALL_LIGER_KERNEL=false
ARG INSTALL_HQQ=false
ARG PIP_INDEX=https://pypi.org/simple
# Set the working directory
WORKDIR /app
# Install the requirements
COPY requirements.txt /app
RUN pip config set global.index-url "$PIP_INDEX" && \
pip config set global.extra-index-url "$PIP_INDEX" && \
python -m pip install --upgrade pip && \
python -m pip install -r requirements.txt
# Copy the rest of the application into the image
COPY . /app
# Install the LLaMA Factory
RUN EXTRA_PACKAGES="metrics"; \
if [ "$INSTALL_BNB" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},bitsandbytes"; \
fi; \
if [ "$INSTALL_VLLM" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},vllm"; \
fi; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
fi; \
if [ "$INSTALL_HQQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
fi; \
pip install -e ".[$EXTRA_PACKAGES]"
# Rebuild flash attention
RUN pip uninstall -y transformer-engine flash-attn && \
if [ "$INSTALL_FLASHATTN" == "true" ]; then \
pip uninstall -y ninja && pip install ninja && \
pip install --no-cache-dir flash-attn --no-build-isolation; \
fi
# Set up volumes
VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]
# Expose port 7860 for the LLaMA Board
ENV GRADIO_SERVER_PORT 7860
EXPOSE 7860
# Expose port 8000 for the API service
ENV API_PORT 8000
EXPOSE 8000
docker/docker-rocm/docker-compose.yml 0 → 100644
View file @ 27a7ad86
services:
llamafactory:
build:
dockerfile: ./docker/docker-rocm/Dockerfile
context: ../..
args:
INSTALL_BNB: false
INSTALL_VLLM: false
INSTALL_DEEPSPEED: false
INSTALL_FLASHATTN: false
INSTALL_LIGER_KERNEL: false
INSTALL_HQQ: false
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../data:/app/data
- ../../output:/app/output
- ../../saves:/app/saves
ports:
- "7860:7860"
- "8000:8000"
ipc: host
tty: true
stdin_open: true
command: bash
devices:
- /dev/kfd:/dev/kfd
- /dev/dri:/dev/dri
restart: unless-stopped
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