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add logo
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![# LLaMA Factory](assets/logo.png)
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rename repository
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[![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)
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[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
update readme
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[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
update readme, add starcoder2, cosmopedia
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[![Citation](https://img.shields.io/badge/citation-21-green)](#使用了-llama-factory-的项目)
rename repository
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[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
update dc link
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[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
add twitter
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[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
update readme, add starcoder2, cosmopedia
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[![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)
add colab demo
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[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
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Update README_zh.md
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👋 加入我们的[微信群](assets/wechat.jpg)。
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\[ [English](README.md) | 中文 \]
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**微调大模型可以像这样轻松…**
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https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd-d76c6d0a6594
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选择你的打开方式：
- **Hugging Face 空间**：https://huggingface.co/spaces/hiyouga/LLaMA-Board
- **魔搭社区**：https://modelscope.cn/studios/hiyouga/LLaMA-Board
- **Colab**：https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
- **本地机器**：请见[如何使用](#如何使用)
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## 目录
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- [项目特色](#项目特色)
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- [性能指标](#性能指标)
- [更新日志](#更新日志)
- [模型](#模型)
- [训练方法](#训练方法)
- [数据集](#数据集)
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- [软硬件依赖](#软硬件依赖)
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- [如何使用](#如何使用)
- [使用了 LLaMA Factory 的项目](#使用了-llama-factory-的项目)
- [协议](#协议)
- [引用](#引用)
- [致谢](#致谢)
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## 项目特色
- **多种模型**：LLaMA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
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- **集成方法**：（增量）预训练、指令监督微调、奖励模型训练、PPO 训练和 DPO 训练。
- **多种精度**：32 比特全参数微调、16 比特冻结微调、16 比特 LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8 的 2/4/8 比特 QLoRA 微调。
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- **先进算法**：GaLore、DoRA、LongLoRA、LLaMA Pro、LoRA+、LoftQ 和 Agent 微调。
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- **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
add examples
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- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
support vllm
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- **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
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## 性能指标
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与 ChatGLM 官方的 [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning) 微调相比，LLaMA-Factory 的 LoRA 微调提供了 **3.7 倍**的加速比，同时在广告文案生成任务上取得了更高的 Rouge 分数。结合 4 比特量化技术，LLaMA-Factory 的 QLoRA 微调进一步降低了 GPU 显存消耗。
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![benchmark](assets/benchmark.svg)
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<details><summary>变量定义</summary>
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- **Training Speed**: 训练阶段每秒处理的样本数量。（批处理大小=4，截断长度=1024）
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- **Rouge Score**: [广告文案生成](https://aclanthology.org/D19-1321.pdf)任务验证集上的 Rouge-2 分数。（批处理大小=4，截断长度=1024）
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- **GPU Memory**: 4 比特量化训练的 GPU 显存峰值。（批处理大小=1，截断长度=1024）
add benchmark
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- 我们在 ChatGLM 的 P-Tuning 中采用 `pre_seq_len=128`，在 LLaMA-Factory 的 LoRA 微调中采用 `lora_rank=32`。
patch modelscope
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</details>
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## 更新日志
paper release
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[23/03/21] 我们的论文 "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" 可在 arXiv 上查看！
update readme
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[24/03/20] 我们支持了能在 2x24GB GPU 上微调 70B 模型的 **FSDP+QLoRA**。详细用法请参照 `examples/fsdp_qlora`。
support fsdp + qlora
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improve lora+ impl.
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[24/03/13] 我们支持了 **[LoRA+](https://arxiv.org/abs/2402.12354)**。请使用 `loraplus_lr_ratio=16.0` 参数开启 LoRA+ 方法。
fix galore
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[24/03/07] 我们支持了梯度低秩投影（**[GaLore](https://arxiv.org/abs/2403.03507)**）算法。请使用 `--use_galore` 参数切换显存高效的优化器。
support galore
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add galore examples
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[24/03/07] 我们集成了 **[vLLM](https://github.com/vllm-project/vllm)** 以实现极速并发推理。请使用 `--infer_backend vllm` 来获得 **270%** 的推理速度。（尚不支持 LoRA，请先合并权重。）
support vllm
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support fsdp + qlora
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<details><summary>展开日志</summary>
support DoRA, AWQ, AQLM #2512
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support fsdp + qlora
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[24/02/28] 我们支持了 **[DoRA](https://arxiv.org/abs/2402.09353)** 微调。请使用 `--use_dora` 参数进行 DoRA 微调。
support llama pro #2338 , add rslora
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support fsdp + qlora
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[24/02/15] 我们支持了 [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro) 提出的**块扩展**方法。详细用法请参照 `examples/extras/llama_pro`。
support llama pro #2338 , add rslora
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support vllm
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[24/02/05] Qwen1.5（Qwen2 测试版）系列模型已在 LLaMA-Factory 中实现微调支持。详情请查阅该[博客页面](https://qwenlm.github.io/zh/blog/qwen1.5/)。
update readme
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[24/01/18] 我们针对绝大多数模型实现了 **Agent 微调**，微调时指定 `--dataset glaive_toolcall` 即可使模型获得工具调用能力。
support vllm
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[23/12/23] 我们针对 LLaMA, Mistral 和 Yi 模型支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的 LoRA 训练加速。请使用 `--use_unsloth` 参数启用 unsloth 优化。该方法可提供 **170%** 的训练速度，详情请查阅[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
support unsloth
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update readme
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[23/12/12] 我们支持了微调最新的混合专家模型 **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)**。硬件需求请查阅[此处](#硬件依赖)。
support mixtral
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fix pretrain data loader
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[23/12/01] 我们支持了从 **[魔搭社区](https://modelscope.cn/models)** 下载预训练模型和数据集。详细用法请参照 [此教程](#使用魔搭社区可跳过)。
update readme
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[23/10/21] 我们支持了 **[NEFTune](https://arxiv.org/abs/2310.05914)** 训练技巧。请使用 `--neftune_noise_alpha` 参数启用 NEFTune，例如 `--neftune_noise_alpha 5`。
fix #1068 #1074
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[23/09/27] 我们针对 LLaMA 模型支持了 [LongLoRA](https://github.com/dvlab-research/LongLoRA) 提出的 **$S^2$-Attn**。请使用 `--shift_attn` 参数以启用该功能。
support LongLoRA
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add CMMLU, update eval script
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[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。使用方法请参阅[此示例](#模型评估)。
support FlashAttention2
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support loftq
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[23/09/10] 我们支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `--flash_attn` 参数以启用 FlashAttention-2。
update readme
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add MMLU and C-Eval script
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[23/08/12] 我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请使用 `--rope_scaling linear` 参数训练模型或使用 `--rope_scaling dynamic` 参数评估模型。
support DPO training (2305.18290)
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add MMLU and C-Eval script
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[23/08/11] 我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。使用方法请参阅[此示例](#dpo-训练)。
support loftq
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[23/07/31] 我们支持了**数据流式加载**。请使用 `--streaming` 和 `--max_steps 10000` 参数来流式加载数据集。
update readme
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fix chatglm2 tokenizer
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[23/07/29] 我们在 Hugging Face 发布了两个 13B 指令微调模型。详细内容请查阅我们的 Hugging Face 项目（[LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)）。
release v0.1.4
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reimplement neftune
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[23/07/18] 我们开发了支持训练和测试的**浏览器一体化界面**。请使用 `train_web.py` 在您的浏览器中微调模型。感谢 [@KanadeSiina](https://github.com/KanadeSiina) 和 [@codemayq](https://github.com/codemayq) 在该功能开发中付出的努力。
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update readme
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[23/07/09] 我们开源了 **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹，一个简单易用的、能迅速编辑大模型事实记忆的工具包。如果您感兴趣请关注我们的 [FastEdit](https://github.com/hiyouga/FastEdit) 项目。
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fix chatglm2 tokenizer
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[23/06/29] 我们提供了一个**可复现的**指令模型微调示例，详细内容请查阅 [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft)。
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update readme
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[23/06/22] 我们对齐了[示例 API](src/api_demo.py) 与 [OpenAI API](https://platform.openai.com/docs/api-reference/chat) 的格式，您可以将微调模型接入**任意基于 ChatGPT 的应用**中。
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reimplement neftune
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[23/06/03] 我们实现了 4 比特的 LoRA 训练（也称 **[QLoRA](https://github.com/artidoro/qlora)**）。请使用 `--quantization_bit 4` 参数进行 4 比特量化微调。
根据GLM Efficient Tuning添加中文README，web添加了server_port
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patch modelscope
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</details>
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## 模型
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| 模型名 | 模型大小 | 默认模块 | Template |
| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
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| [Baichuan2](https://huggingface.co/baichuan-inc) | 7B/13B | W_pack | baichuan2 |
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| [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
update readme
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| [ChatGLM3](https://huggingface.co/THUDM/chatglm3-6b) | 6B | query_key_value | chatglm3 |
tiny fix
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| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B | q_proj,v_proj | deepseek |
update readme
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| [Falcon](https://huggingface.co/tiiuae) | 7B/40B/180B | query_key_value | falcon |
support gemma
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| [Gemma](https://huggingface.co/google) | 2B/7B | q_proj,v_proj | gemma |
update readme
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| [InternLM2](https://huggingface.co/internlm) | 7B/20B | wqkv | intern2 |
update readme (list in alphabetical order)
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| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - |
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 |
update templates
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| [Mistral](https://huggingface.co/mistralai) | 7B | q_proj,v_proj | mistral |
support mixtral
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| [Mixtral](https://huggingface.co/mistralai) | 8x7B | q_proj,v_proj | mistral |
support olmo
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| [OLMo](https://huggingface.co/allenai) | 1B/7B | att_proj | olmo |
fix phi modules
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| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | q_proj,v_proj | - |
update readme
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| [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | c_attn | qwen |
support qwen1.5
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| [Qwen1.5](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/72B | q_proj,v_proj | qwen |
update readme, add starcoder2, cosmopedia
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| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | q_proj,v_proj | - |
update readme
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| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | q_proj,v_proj | xverse |
add Yi-9B model
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| [Yi](https://huggingface.co/01-ai) | 6B/9B/34B | q_proj,v_proj | yi |
add yuan model
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| [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | q_proj,v_proj | yuan |
update readme
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update readme
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> [!NOTE]
support FlashAttention2
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> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块。
>
update readme
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> 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”（Chat）模型请务必使用**对应的模板**。
fix vicuna template
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update readme and constants
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项目所支持模型的完整列表请参阅 [constants.py](src/llmtuner/extras/constants.py)。
support DPO training (2305.18290)
2023-08-11 03:02:53 +08:00
update readme
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您也可以在 [template.py](src/llmtuner/data/template.py) 中添加自己的对话模板。
support DPO training (2305.18290)
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## 训练方法
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| 方法 | 全参数训练 | 部分参数训练 | LoRA | QLoRA |
| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
| 预训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| 指令监督微调 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
support full-parameter PPO
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| 奖励模型训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| PPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| DPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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update readme
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> [!NOTE]
update readme
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> 请使用 `--quantization_bit 4` 参数来启用 QLoRA 训练。
update readme
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update readme, fix web ui postprocess
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## 数据集
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support sharegpt format, add datasets
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<details><summary>预训练数据集</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)
- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
</details>
<details><summary>指令微调数据集</summary>
- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
improve fix tokenizer
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- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
- [Self Cognition (zh)](data/self_cognition.json)
support sharegpt format, add datasets
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- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
- [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)
- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
- [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)
update readme and constants
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- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
improve aligner
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- [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca)
support sharegpt format, add datasets
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- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
improve fix tokenizer
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- [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa)
support sharegpt format, add datasets
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- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
fix #1696
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- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
tiny update
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- [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
support sharegpt format, add datasets
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- [Ad Gen (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)
update data readme (zh)
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- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
support sharegpt format, add datasets
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- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
update readme
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- [Glaive Function Calling V2 (en)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
update readme, add starcoder2, cosmopedia
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- [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
improve fix tokenizer
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- [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)
support sharegpt format, add datasets
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</details>
<details><summary>偏好数据集</summary>
- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
support fsdp + qlora
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- [Orca DPO (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
fix #1696
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- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
improve fix tokenizer
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- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
support sharegpt format, add datasets
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</details>
使用方法请参考 [data/README_zh.md](data/README_zh.md) 文件。
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部分数据集的使用需要确认，我们推荐使用下述命令登录您的 Hugging Face 账户。
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```bash
pip install --upgrade huggingface_hub
huggingface-cli login
```
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## 软硬件依赖
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| 必需项 | 至少 | 推荐 |
| ------------ | ------- | --------- |
| python | 3.8 | 3.10 |
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| torch | 1.13.1 | 2.2.0 |
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| transformers | 4.37.2 | 4.38.2 |
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| datasets | 2.14.3 | 2.17.1 |
| accelerate | 0.27.2 | 0.27.2 |
| peft | 0.9.0 | 0.9.0 |
| trl | 0.7.11 | 0.7.11 |
| 可选项 | 至少 | 推荐 |
| ------------ | ------- | --------- |
| CUDA | 11.6 | 12.2 |
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| deepspeed | 0.10.0 | 0.13.1 |
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| bitsandbytes | 0.39.0 | 0.41.3 |
| flash-attn | 2.3.0 | 2.5.5 |
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### 硬件依赖
add examples
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\* *估算值*
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| 训练方法 | 精度 | 7B | 13B | 30B | 70B | 8x7B |
support mixtral
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| ------- | ---- | ----- | ----- | ----- | ------ | ------ |
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| 全参数 | AMP | 120GB | 240GB | 600GB | 1200GB | 900GB |
| 全参数 | 16 | 60GB | 120GB | 300GB | 600GB | 400GB |
support layerwise galore
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| GaLore | 16 | 16GB | 32GB | 64GB | 160GB | 120GB |
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| 部分参数 | 16 | 20GB | 40GB | 80GB | 200GB | 160GB |
| LoRA | 16 | 16GB | 32GB | 64GB | 160GB | 120GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 60GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 30GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 18GB |
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## 如何使用
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### 数据准备（可跳过）
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support sharegpt format, add datasets
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关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。构建自定义数据集时，既可以使用单个 `.json` 文件，也可以使用一个[数据加载脚本](https://huggingface.co/docs/datasets/dataset_script)和多个文件。
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> [!NOTE]
support sharegpt format, add datasets
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> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件，该文件的格式请参考 `data/README_zh.md`。
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### 环境搭建（可跳过）
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```bash
rename repository
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git clone https://github.com/hiyouga/LLaMA-Factory.git
conda create -n llama_factory python=3.10
conda activate llama_factory
cd LLaMA-Factory
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pip install -r requirements.txt
```
Update README_zh.md
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如果要在 Windows 平台上开启量化 LoRA（QLoRA），需要安装预编译的 `bitsandbytes` 库, 支持 CUDA 11.1 到 12.2。
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```bash
1. update the version of pre-built bitsandbytes library 2. add pre-built flash-attn library
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pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.40.0-py3-none-win_amd64.whl
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```
Update README_zh.md
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如果要在 Windows 平台上开启 FlashAttention-2，需要安装预编译的 `flash-attn` 库，支持 CUDA 12.1 到 12.2，请根据需求到 [flash-attention](https://github.com/bdashore3/flash-attention/releases) 下载对应版本安装。
1. update the version of pre-built bitsandbytes library 2. add pre-built flash-attn library
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patch modelscope
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### 使用魔搭社区（可跳过）
add readme
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support system column #1765
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如果您在 Hugging Face 模型和数据集的下载中遇到了问题，可以通过下述方法使用魔搭社区。
add readme
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patch modelscope
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```bash
export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
add readme
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```
patch modelscope
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接着即可通过指定模型名称来训练对应的模型。（在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型）
add readme
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patch modelscope
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```bash
update readme
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
patch modelscope
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--model_name_or_path modelscope/Llama-2-7b-ms \
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... # 参数同下
add readme
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```
support system column #1765
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LLaMA Board 同样支持魔搭社区的模型和数据集下载。
add readme
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patch modelscope
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```bash
CUDA_VISIBLE_DEVICES=0 USE_MODELSCOPE_HUB=1 python src/train_web.py
add readme
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```
update readme
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### 单 GPU 训练
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> [!IMPORTANT]
support FlashAttention2
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> 如果您使用多张 GPU 训练模型，请移步[多 GPU 分布式训练](#多-gpu-分布式训练)部分。
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#### LLaMA Board GUI
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_web.py
```
update readme
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#### 预训练
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```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage pt \
--do_train \
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--model_name_or_path path_to_llama_model \
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--dataset wiki_demo \
--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir path_to_pt_checkpoint \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--fp16
```
update readme
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#### 指令监督微调
根据GLM Efficient Tuning添加中文README，web添加了server_port
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```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft \
--do_train \
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--model_name_or_path path_to_llama_model \
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--dataset alpaca_gpt4_zh \
--template default \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir path_to_sft_checkpoint \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--fp16
```
update readme
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#### 奖励模型训练
根据GLM Efficient Tuning添加中文README，web添加了server_port
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```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage rm \
--do_train \
refactor adapter hparam
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--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
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--dataset comparison_gpt4_zh \
--template default \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir path_to_rm_checkpoint \
support DPO training (2305.18290)
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--per_device_train_batch_size 2 \
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--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
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--learning_rate 1e-6 \
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--num_train_epochs 1.0 \
--plot_loss \
--fp16
```
update readme
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#### PPO 训练
根据GLM Efficient Tuning添加中文README，web添加了server_port
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```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage ppo \
--do_train \
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--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
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--dataset alpaca_gpt4_zh \
--template default \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--reward_model path_to_rm_checkpoint \
--output_dir path_to_ppo_checkpoint \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
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--top_k 0 \
--top_p 0.9 \
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--logging_steps 10 \
--save_steps 1000 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
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--plot_loss \
--fp16
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```
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> [!TIP]
> 使用 `--adapter_name_or_path path_to_sft_checkpoint,path_to_ppo_checkpoint` 来进行微调模型的推理。
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> [!WARNING]
support GPTQ tuning #729 #1481 #1545 , fix chatglm template #1453 #1480 #1569
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> 如果使用 fp16 精度进行 LLaMA-2 模型的 PPO 训练，请使用 `--per_device_train_batch_size=1`。
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#### DPO 训练
support DPO training (2305.18290)
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```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage dpo \
--do_train \
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--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
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--dataset comparison_gpt4_zh \
--template default \
--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir path_to_dpo_checkpoint \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--plot_loss \
--fp16
```
update readme
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> [!TIP]
> 使用 `--adapter_name_or_path path_to_sft_checkpoint,path_to_dpo_checkpoint` 来进行微调模型的推理。
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### 多 GPU 分布式训练
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#### 使用 Huggingface Accelerate
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```bash
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accelerate launch --config_file config.yaml src/train_bash.py \
--ddp_timeout 180000000 \
... # 参数同上
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```
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<details><summary>使用 Accelerate 进行 LoRA 训练的 config.yaml 示例</summary>
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```yaml
compute_environment: LOCAL_MACHINE
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debug: false
support FlashAttention2
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distributed_type: MULTI_GPU
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downcast_bf16: 'no'
support FlashAttention2
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gpu_ids: all
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machine_rank: 0
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```
</details>
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> [!TIP]
> 我们推荐使用 Accelerate 进行 LoRA 训练。
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#### 使用 DeepSpeed
```bash
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deepspeed --num_gpus 8 src/train_bash.py \
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--deepspeed ds_config.json \
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--ddp_timeout 180000000 \
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... # 参数同上
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```
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<details><summary>使用 DeepSpeed ZeRO-2 进行全参数训练的 ds_config.json 示例</summary>
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```json
{
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"train_batch_size": "auto",
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"train_micro_batch_size_per_gpu": "auto",
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"zero_allow_untested_optimizer": true,
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
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"initial_scale_power": 16,
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"hysteresis": 2,
"min_loss_scale": 1
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},
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"bf16": {
"enabled": "auto"
},
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"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 5e8,
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"overlap_comm": true,
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"reduce_scatter": true,
"reduce_bucket_size": 5e8,
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"contiguous_gradients": true,
"round_robin_gradients": true
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}
}
```
</details>
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> [!TIP]
> 更多训练脚本请查看 [examples](examples)。
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### 合并 LoRA 权重并导出模型
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```bash
support vllm
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CUDA_VISIBLE_DEVICES=0 python src/export_model.py \
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--model_name_or_path path_to_llama_model \
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--adapter_name_or_path path_to_checkpoint \
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--template default \
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--finetuning_type lora \
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--export_dir path_to_export \
--export_size 2 \
--export_legacy_format False
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```
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> [!WARNING]
support quantization in export model
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> 尚不支持量化模型的 LoRA 权重合并及导出。
> [!TIP]
update readme
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> 仅使用 `--model_name_or_path path_to_export` 来加载导出后的模型。
>
update readme
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> 合并 LoRA 权重之后可再次使用 `--export_quantization_bit 4` 和 `--export_quantization_dataset data/c4_demo.json` 基于 AutoGPTQ 量化模型。
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update readme
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### 使用 OpenAI 风格 API 推理
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```bash
update readme
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CUDA_VISIBLE_DEVICES=0 API_PORT=8000 python src/api_demo.py \
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--model_name_or_path path_to_llama_model \
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--adapter_name_or_path path_to_checkpoint \
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--template default \
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--finetuning_type lora
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```
update ppo trainer
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> [!TIP]
support FlashAttention2
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> 关于 API 文档请见 `http://localhost:8000/docs`。
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### 使用命令行推理
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```bash
support vllm
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CUDA_VISIBLE_DEVICES=0 python src/cli_demo.py \
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--model_name_or_path path_to_llama_model \
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--adapter_name_or_path path_to_checkpoint \
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--template default \
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--finetuning_type lora
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```
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### 使用浏览器推理
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```bash
support vllm
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CUDA_VISIBLE_DEVICES=0 python src/web_demo.py \
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--model_name_or_path path_to_llama_model \
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--adapter_name_or_path path_to_checkpoint \
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--template default \
refactor adapter hparam
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--finetuning_type lora
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```
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### 模型评估
```bash
CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
--model_name_or_path path_to_llama_model \
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--adapter_name_or_path path_to_checkpoint \
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--template vanilla \
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--finetuning_type lora \
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--task ceval \
--split validation \
--lang zh \
--n_shot 5 \
--batch_size 4
```
### 模型预测
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft \
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--do_predict \
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--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
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--dataset alpaca_gpt4_zh \
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--template default \
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--finetuning_type lora \
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--output_dir path_to_predict_result \
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--per_device_eval_batch_size 1 \
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--max_samples 100 \
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--predict_with_generate \
--fp16
update readme
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```
update ppo trainer
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> [!WARNING]
support GPTQ tuning #729 #1481 #1545 , fix chatglm template #1453 #1480 #1569
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> 如果使用 fp16 精度进行 LLaMA-2 模型的预测，请使用 `--per_device_eval_batch_size=1`。
update ppo trainer
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> [!TIP]
add CMMLU, update eval script
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> 我们建议在量化模型的预测中使用 `--per_device_eval_batch_size=1` 和 `--max_target_length 128`。
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add projects
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## 使用了 LLaMA Factory 的项目
add papers
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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)
1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
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)
add papers
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1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
add papers
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1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
add papers
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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. 2024. [[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)
add papers
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1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: 天文大模型 StarWhisper，基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。
1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: 中文法律领域大模型 DISC-LawLLM，基于 Baichuan-13B 微调而得，具有法律推理和知识检索能力。
1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
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。
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。
fix phi modules
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update ppo trainer
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> [!TIP]
add benchmark
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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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## 协议
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本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。
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support olmo
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使用模型权重时，请遵循对应的模型协议：[Baichuan2](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) / [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) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](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) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
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## 引用
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如果您觉得此项目有帮助，请考虑以下列格式引用
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```bibtex
paper release
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@article{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},
journal={arXiv preprint arXiv:2403.13372},
year={2024},
url={http://arxiv.org/abs/2403.13372}
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}
```
## 致谢
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本项目受益于 [PEFT](https://github.com/huggingface/peft)、[QLoRA](https://github.com/artidoro/qlora) 和 [FastChat](https://github.com/lm-sys/FastChat)，感谢以上诸位作者的付出。
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## Star History
rename repository
2023-10-12 21:42:29 +08:00
![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date)