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![# LLaMA Factory ](assets/logo.png )
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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:
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- **Colab**: https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
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- **PAI-DSW**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
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- **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
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- **多种模型**: LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
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- **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。
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- **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、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 Agent 微调。
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- **实用技巧**: FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
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- **实验监控**: LlamaBoard、TensorBoard、Wandb、MLflow 等等。
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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)
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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` 。
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< / details >
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## 更新日志
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[24/06/07] 我们支持了 ** [Qwen-2 ](https://qwenlm.github.io/blog/qwen2/ )** 系列模型的微调。
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[24/06/05] 我们支持了 ** [GLM-4-9B/GLM-4-9B-Chat ](https://github.com/THUDM/GLM-4 )** 模型的微调。
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[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 )。
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< details > < summary > 展开日志< / summary >
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[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。
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[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 )。
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[24/05/14] 我们支持了昇腾 NPU 设备的训练和推理。详情请查阅[安装](#安装-llama-factory)部分。
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[24/04/26] 我们支持了多模态模型 **LLaVA-1.5** 的微调。详细用法请参照 [examples ](examples/README_zh.md )。
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[24/04/22] 我们提供了在免费 T4 GPU 上微调 Llama-3 模型的 ** [Colab 笔记本 ](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing )**。Hugging Face 社区公开了两个利用 LLaMA Factory 微调的 Llama-3 模型,详情请见 [Llama3-8B-Chinese-Chat ](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat ) 和 [Llama3-Chinese ](https://huggingface.co/zhichen/Llama3-Chinese )。
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[24/04/21] 我们基于 [AstraMindAI 的仓库 ](https://github.com/astramind-ai/Mixture-of-depths )支持了 ** [混合深度训练 ](https://arxiv.org/abs/2404.02258 )**。详细用法请参照 [examples ](examples/README_zh.md )。
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[24/04/16] 我们支持了 ** [BAdam ](https://arxiv.org/abs/2404.02827 )**。详细用法请参照 [examples ](examples/README_zh.md )。
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[24/04/16] 我们支持了 ** [unsloth ](https://github.com/unslothai/unsloth )** 的长序列训练( 24GB 可训练 Llama-2-7B-56k) 。该方法相比 FlashAttention-2 提供了 **117%** 的训练速度和 **50%** 的显存节约。更多数据请见[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
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[24/03/31] 我们支持了 ** [ORPO ](https://arxiv.org/abs/2403.07691 )**。详细用法请参照 [examples ](examples/README_zh.md )。
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[24/03/21] 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!
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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 ](examples/README_zh.md )。
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[24/03/13] 我们支持了 ** [LoRA+ ](https://arxiv.org/abs/2402.12354 )**。详细用法请参照 [examples ](examples/README_zh.md )。
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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。详细用法请参照 [examples ](examples/README_zh.md )。
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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。
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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: true` 参数进行 DoRA 微调。
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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 ](examples/README_zh.md )。
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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/)。
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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_zh` 即可使模型获得工具调用能力。
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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: true` 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)。
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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)。
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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 ](#从魔搭社区下载 )。
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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: 5` 参数启用 NEFTune。
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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: true` 参数以启用该功能。
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[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。详细用法请参照 [examples ](examples/README_zh.md )。
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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: fa2` 参数以启用 FlashAttention-2。
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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。
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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 )**。详细用法请参照 [examples ](examples/README_zh.md )。
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[23/07/31] 我们支持了**数据流式加载**。请使用 `streaming: true` 和 `max_steps: 10000` 参数来流式加载数据集。
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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 ))。
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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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[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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[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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[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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[23/06/03] 我们实现了 4 比特的 LoRA 训练(也称 ** [QLoRA ](https://github.com/artidoro/qlora )**)。详细用法请参照 [examples ](examples/README_zh.md )。
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< / details >
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## 模型
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| 模型名 | 模型大小 | Template |
| -------------------------------------------------------- | -------------------------------- | --------- |
| [Baichuan2 ](https://huggingface.co/baichuan-inc ) | 7B/13B | baichuan2 |
| [BLOOM ](https://huggingface.co/bigscience ) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [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 (MoE) ](https://huggingface.co/deepseek-ai ) | 7B/16B/67B/236B | deepseek |
| [Falcon ](https://huggingface.co/tiiuae ) | 7B/11B/40B/180B | falcon |
| [Gemma/CodeGemma ](https://huggingface.co/google ) | 2B/7B | gemma |
| [GLM4 ](https://huggingface.co/THUDM ) | 9B | glm4 |
| [InternLM2 ](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 ](https://huggingface.co/meta-llama ) | 8B/70B | llama3 |
| [LLaVA-1.5 ](https://huggingface.co/llava-hf ) | 7B/13B | vicuna |
| [Mistral/Mixtral ](https://huggingface.co/mistralai ) | 7B/8x7B/8x22B | mistral |
| [OLMo ](https://huggingface.co/allenai ) | 1B/7B | - |
| [PaliGemma ](https://huggingface.co/google ) | 3B | gemma |
| [Phi-1.5/2 ](https://huggingface.co/microsoft ) | 1.3B/2.7B | - |
| [Phi-3 ](https://huggingface.co/microsoft ) | 4B/7B/14B | phi |
| [Qwen ](https://huggingface.co/Qwen ) | 1.8B/7B/14B/72B | qwen |
| [Qwen1.5 (Code/MoE) ](https://huggingface.co/Qwen ) | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | qwen |
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| [Qwen2 (MoE) ](https://huggingface.co/Qwen ) | 0.5B/1.5B/7B/57B/72B | qwen |
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| [StarCoder2 ](https://huggingface.co/bigcode ) | 3B/7B/15B | - |
| [XVERSE ](https://huggingface.co/xverse ) | 7B/13B/65B | xverse |
| [Yi (1/1.5) ](https://huggingface.co/01-ai ) | 6B/9B/34B | yi |
| [Yi-VL ](https://huggingface.co/01-ai ) | 6B/34B | yi_vl |
| [Yuan ](https://huggingface.co/IEITYuan ) | 2B/51B/102B | yuan |
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> [!NOTE]
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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”( Instruct/Chat) 模型请务必使用**对应的模板**。
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>
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> 请务必在训练和推理时采用**完全一致**的模板。
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项目所支持模型的完整列表请参阅 [constants.py ](src/llamafactory/extras/constants.py )。
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您也可以在 [template.py ](src/llamafactory/data/template.py ) 中添加自己的对话模板。
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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: |
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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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| KTO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| ORPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| SimPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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## 数据集
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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 )
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- [FineWeb (en) ](https://huggingface.co/datasets/HuggingFaceFW/fineweb )
- [FineWeb-Edu (en) ](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu )
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- [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 >
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- [Identity (en&zh) ](data/identity.json )
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- [Stanford Alpaca (en) ](https://github.com/tatsu-lab/stanford_alpaca )
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- [Stanford Alpaca (zh) ](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3 )
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- [Alpaca GPT4 (en&zh) ](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM )
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- [Glaive Function Calling V2 (en&zh) ](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2 )
- [LIMA (en) ](https://huggingface.co/datasets/GAIR/lima )
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- [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 )
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- [OpenOrca (en) ](https://huggingface.co/datasets/Open-Orca/OpenOrca )
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- [SlimOrca (en) ](https://huggingface.co/datasets/Open-Orca/SlimOrca )
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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 )
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- [Wiki QA (en) ](https://huggingface.co/datasets/wiki_qa )
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- [Web QA (zh) ](https://huggingface.co/datasets/suolyer/webqa )
- [WebNovel (zh) ](https://huggingface.co/datasets/zxbsmk/webnovel_cn )
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- [Nectar (en) ](https://huggingface.co/datasets/berkeley-nest/Nectar )
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- [deepctrl (en&zh) ](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data )
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- [Advertise Generating (zh) ](https://huggingface.co/datasets/HasturOfficial/adgen )
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- [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 )
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- [LMSYS Chat 1M (en) ](https://huggingface.co/datasets/lmsys/lmsys-chat-1m )
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- [Evol Instruct V2 (en) ](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k )
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- [Cosmopedia (en) ](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia )
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- [STEM (zh) ](https://huggingface.co/datasets/hfl/stem_zh_instruction )
- [Ruozhiba (zh) ](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo )
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- [LLaVA mixed (en&zh) ](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k )
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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 )
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< / details >
< details > < summary > 偏好数据集< / summary >
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- [DPO mixed (en&zh) ](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k )
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- [UltraFeedback (en) ](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized )
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- [Orca DPO Pairs (en) ](https://huggingface.co/datasets/Intel/orca_dpo_pairs )
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- [HH-RLHF (en) ](https://huggingface.co/datasets/Anthropic/hh-rlhf )
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- [Nectar (en) ](https://huggingface.co/datasets/berkeley-nest/Nectar )
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- [Orca DPO (de) ](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de )
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- [KTO mixed (en) ](https://huggingface.co/datasets/argilla/kto-mix-15k )
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< / details >
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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 |
| ------------ | ------- | --------- |
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| python | 3.8 | 3.11 |
| torch | 1.13.1 | 2.3.0 |
| transformers | 4.41.2 | 4.41.2 |
| datasets | 2.16.0 | 2.19.2 |
| accelerate | 0.30.1 | 0.30.1 |
| peft | 0.11.1 | 0.11.1 |
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| trl | 0.8.6 | 0.9.4 |
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| 可选项 | 至少 | 推荐 |
| ------------ | ------- | --------- |
| CUDA | 11.6 | 12.2 |
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| deepspeed | 0.10.0 | 0.14.0 |
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| bitsandbytes | 0.39.0 | 0.43.1 |
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| vllm | 0.4.3 | 0.4.3 |
| flash-attn | 2.3.0 | 2.5.9 |
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### 硬件依赖
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\* *估算值*
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| 方法 | 精度 | 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 |
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## 如何使用
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### 安装 LLaMA Factory
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> [!IMPORTANT]
> 此步骤为必需。
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```bash
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git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
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cd LLaMA-Factory
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pip install -e '.[torch,metrics]'
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```
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可选的额外依赖项: torch、torch_npu、metrics、deepspeed、bitsandbytes、vllm、galore、badam、gptq、awq、aqlm、qwen、modelscope、quality
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> [!TIP]
> 遇到包冲突时,可使用 `pip install --no-deps -e .` 解决。
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< details > < summary > Windows 用户指南< / summary >
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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, 请根据您的 CUDA 版本情况选择适合的[发布版本](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels)。
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```bash
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pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
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```
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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。
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< / details >
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< details > < summary > 昇腾 NPU 用户指南< / summary >
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加入 [NPU 用户群 ](assets/wechat_npu.jpg )。
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在昇腾 NPU 设备上安装 LLaMA Factory 时,需要指定额外依赖项,使用 `pip install -e '.[torch-npu,metrics]'` 命令安装。此外,还需要安装 ** [Ascend CANN Toolkit and Kernels ](https://www.hiascend.com/developer/download/community/result?module=cann )**,安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令:
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```bash
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# 请替换 URL 为 CANN 版本和设备型号对应的 URL
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# 安装 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-"$(uname -i)".run
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bash Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run --install
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# 安装 CANN Kernels
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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
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bash Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run --install
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# 设置环境变量
source /usr/local/Ascend/ascend-toolkit/set_env.sh
```
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| 依赖项 | 至少 | 推荐 |
| ------------ | ------- | ----------- |
| 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 |
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Docker 镜像:
- 32GB: [下载地址](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html)
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- 64GB: [下载地址](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
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请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。
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如果遇到无法正常推理的情况,请尝试设置 `do_sample: false` 。
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< / details >
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### 数据准备
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。
> [!NOTE]
> 使用自定义数据集时,请更新 `data/dataset_info.json` 文件。
### 快速开始
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下面三行命令分别对 Llama3-8B-Instruct 模型进行 LoRA **微调** 、**推理**和**合并**。
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```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
```
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高级用法请参考 [examples/README_zh.md ](examples/README_zh.md )(包括多 GPU 微调)。
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> [!TIP]
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> 使用 `llamafactory-cli help` 显示帮助信息。
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### LLaMA Board 可视化微调(由 [Gradio](https://github.com/gradio-app/gradio) 驱动)
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#### 使用本地环境
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```bash
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CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
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```
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#### 使用 Docker
```bash
docker build -f ./Dockerfile -t llama-factory:latest .
docker run --gpus=all \
-v ./hf_cache:/root/.cache/huggingface/ \
-v ./data:/app/data \
-v ./output:/app/output \
-p 7860:7860 \
--shm-size 16G \
--name llama_factory \
-d llama-factory:latest
```
#### 使用 Docker Compose
```bash
docker compose -f ./docker-compose.yml up -d
```
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< details > < summary > 数据卷详情< / summary >
- hf_cache: 使用宿主机的 Hugging Face 缓存文件夹,允许更改为新的目录。
- data: 宿主机中存放数据集的文件夹路径。
- output: 将导出目录设置为该路径后, 即可在宿主机中访问导出后的模型。
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< / details >
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### 利用 vLLM 部署 OpenAI API
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```bash
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CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
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```
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> [!TIP]
> API 文档请查阅 https://platform.openai.com/docs/api-reference/chat/create。
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### 从魔搭社区下载
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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。
```bash
export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
```
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将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型,例如 `LLM-Research/Meta-Llama-3-8B-Instruct` 。
### 使用 W&B 面板
若要使用 [Weights & Biases ](https://wandb.ai ) 记录实验数据,请添加下面的参数。
```yaml
report_to: wandb
run_name: test_run # 可选
```
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在启动训练任务时,将 `WANDB_API_KEY` 设置为[密钥](https://wandb.ai/authorize)来登录 W& B 账户。
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## 使用了 LLaMA Factory 的项目
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如果您有项目希望添加至下述列表,请通过邮件联系或者创建一个 PR。
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< details > < summary > 点击显示< / summary >
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1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
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1. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [[arxiv]](https://arxiv.org/abs/2308.10526)
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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)
2024-02-25 07:18:58 +00:00
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)
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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)
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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)
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1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
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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)
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1. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2403.09073)
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1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
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1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)
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1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
2024-04-16 10:09:16 +00:00
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)
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1. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. 2024. [[arxiv]](https://arxiv.org/abs/2404.04316)
2024-04-16 10:09:16 +00:00
1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
2024-05-03 16:31:02 +00:00
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. 2024. [[arxiv]](https://arxiv.org/abs/2404.17140)
1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
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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。
2024-05-15 18:17:31 +00:00
1. ** [Sunsimiao ](https://github.com/X-D-Lab/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。
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1. ** [CareGPT ](https://github.com/WangRongsheng/CareGPT )**: 医疗大模型项目 CareGPT, 基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。
1. ** [MachineMindset ](https://github.com/PKU-YuanGroup/Machine-Mindset/ )**: MBTI性格大模型项目, 根据数据集与训练方式让任意 LLM 拥有 16 个不同的性格类型。
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1. ** [Luminia-13B-v3 ](https://huggingface.co/Nekochu/Luminia-13B-v3 )**:一个用于生成 Stable Diffusion 提示词的大型语言模型。[[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
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1. ** [Chinese-LLaVA-Med ](https://github.com/BUAADreamer/Chinese-LLaVA-Med )**:中文多模态医学大模型,基于 LLaVA-1.5-7B 在中文多模态医疗数据上微调而得。
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< / details >
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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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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 ) / [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 ) / [GLM4 ](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/ ) / [Mistral ](LICENSE ) / [OLMo ](LICENSE ) / [Phi-1.5/2 ](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 ) / [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 ) / [Yi-1.5 ](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
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@article {zheng2024llamafactory,
title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
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author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Yongqiang Ma},
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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 )、[TRL](https://github.com/huggingface/trl)、[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
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![Star History Chart ](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date )