{"repo": "cassie-ikk/llama-project", "files": {"README_zh.md": {"0f17531a21154a010739838a1cc09d98185c67b5": {"t": 1786068113.2251878, "n": 58753, "h": "835ee90c118d4267", "c": "![# LLaMA Factory](assets/logo.png)\n\n[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers)\n[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)\n[![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)\n[![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)\n[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)\n[![Citation](https://img.shields.io/badge/citation-730-green)](https://scholar.google.com/scholar?cites=12620864006390196564)\n[![Docker Pulls](https://img.shields.io/docker/pulls/hiyouga/llamafactory)](https://hub.docker.com/r/hiyouga/llamafactory/tags)\n\n[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)\n[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)\n[![GitCode](https://gitcode.com/zhengyaowei/LLaMA-Factory/star/badge.svg)](https://gitcode.com/zhengyaowei/LLaMA-Factory)\n\n[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)\n[![Open in DSW](https://gallery.pai-ml.com/assets/open-in-dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)\n[![Open in Alaya](assets/alaya_new.svg)](https://docs.alayanew.com/docs/documents/newActivities/llamafactory/?utm_source=LLaMA-Factory)\n[![Open in Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)\n[![Open in Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)\n[![Open in Novita](https://img.shields.io/badge/Novita-Deploy%20Template-blue)](https://novita.ai/templates-library/105981?sharer=88115474-394e-4bda-968e-b88e123d0c47)\n\n### 获得[亚马逊](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)、[英伟达](https://developer.nvidia.cn/rtx/ai-toolkit)、[阿里云](https://help.aliyun.com/zh/pai/use-cases/fine-tune-a-llama-3-model-with-llama-factory)等的应用。\n\n
\n\n### 赞助商 ❤️\n\n\n \"Warp\n\n\n#### [Warp，面向开发者的智能终端](https://warp.dev/llama-factory)\n\n[适用于 MacOS、Linux 和 Windows](https://warp.dev/llama-factory)\n\n----\n\n### 使用零代码[命令行](#快速开始)与 [Web UI](#llama-board-可视化微调由-gradio-驱动) 轻松微调百余种大模型\n\n![GitHub Trend](https://trendshift.io/api/badge/repositories/4535)\n\n
\n\n👋 加入我们的[微信群](assets/wechat.jpg)、[NPU 用户群](assets/wechat_npu.jpg)或 [九章智算云算力优惠群](assets/wechat_alaya.png)。\n\n\\[ [English](README.md) | 中文 \\]\n\n**微调大模型可以像这样轻松…**\n\nhttps://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc\n\n选择你的打开方式：\n\n- **入门教程**：https://zhuanlan.zhihu.com/p/695287607\n- **微调视频教程**：https://www.bilibili.com/video/BV1djgRzxEts/\n- **框架文档**：https://llamafactory.readthedocs.io/zh-cn/latest/\n- **框架文档（昇腾 NPU）**：https://ascend.github.io/docs/sources/llamafactory/\n- **Colab（免费）**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing\n- **本地机器**：请见[如何使用](#如何使用)\n- **PAI-DSW（免费试用）**：https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory\n- **九章智算云（算力优惠活动）**：https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory\n\n> [!NOTE]\n> 除上述链接以外的其他网站均为未经许可的第三方网站，请小心甄别。\n\n## 目录\n\n- [项目特色](#项目特色)\n- [官方博客](#官方博客)\n- [更新日志](#更新日志)\n- [模型](#模型)\n- [训练方法](#训练方法)\n- [数据集](#数据集)\n- [软硬件依赖](#软硬件依赖)\n- [如何使用](#如何使用)\n - [安装 LLaMA Factory](#安装-llama-factory)\n - [数据准备](#数据准备)\n - [快速开始](#快速开始)\n - [LLaMA Board 可视化微调](#llama-board-可视化微调由-gradio-驱动)\n - [构建 Docker](#构建-docker)\n - [利用 vLLM 部署 OpenAI API](#利用-vllm-部署-openai-api)\n - [从魔搭社区下载](#从魔搭社区下载)\n - [从魔乐社区下载](#从魔乐社区下载)\n - [使用 W&B 面板](#使用-wb-面板)\n - [使用 SwanLab 面板](#使用-swanlab-面板)\n- [使用了 LLaMA Factory 的项目](#使用了-llama-factory-的项目)\n- [协议](#协议)\n- [引用](#引用)\n- [致谢](#致谢)\n\n## 项目特色\n\n- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Qwen2-VL、DeepSeek、Yi、Gemma、ChatGLM、Phi 等等。\n- **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。\n- **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。\n- **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[APOLLO](https://github.com/zhuhanqing/APOLLO)、[Adam-mini](https://github.com/zyushun/Adam-mini)、[Muon](https://github.com/KellerJordan/Muon)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 PiSSA。\n- **实用技巧**：[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)、[Unsloth](https://github.com/unslothai/unsloth)、[Liger Kernel](https://github.com/linkedin/Liger-Kernel)、RoPE scaling、NEFTune 和 rsLoRA。\n- **广泛任务**：多轮对话、工具调用、图像理解、视觉定位、视频识别和语音理解等等。\n- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow、[SwanLab](https://github.com/SwanHubX/SwanLab) 等等。\n- **极速推理**：基于 [vLLM](https://github.com/vllm-project/vllm) 或 [SGLang](https://github.com/sgl-project/sglang) 的 OpenAI 风格 API、浏览器界面和命令行接口。\n\n### 最新模型的 Day-N 微调适配\n\n| 适配时间 | 模型名称 |\n| ------------ | -------------------------------------------------------------------- |\n| Day 0 | Qwen3 / Qwen2.5-VL / Gemma 3 / GLM-4.1V / InternLM 3 / MiniCPM-o-2.6 |\n| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |\n\n## 官方博客\n\n- [基于 LLaMA-Factory 和 EasyR1 打造一站式无代码大模型强化学习和部署平台 LLM Model Hub](https://aws.amazon.com/cn/blogs/china/building-llm-model-hub-based-on-llamafactory-and-easyr1/)（中文）\n- [使用 LLaMA-Factory 微调 Qwen2.5-VL 实现自动驾驶场景微调](https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory)（中文）\n- [通过亚马逊 SageMaker HyperPod 上的 LLaMA-Factory 增强多模态模型银行文档的视觉信息提取](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)（英文）\n- [Easy Dataset × LLaMA Factory: 让大模型高效学习领域知识](https://buaa-act.feishu.cn/wiki/KY9xwTGs1iqHrRkjXBwcZP9WnL9)（中文）\n\n
全部博客\n\n- [LLaMA Factory：微调 DeepSeek-R1-Distill-Qwen-7B 模型实现新闻标题分类器](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)（中文）\n- [基于 Amazon SageMaker 和 LLaMA-Factory 打造一站式无代码模型微调部署平台 Model Hub](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)（中文）\n- [LLaMA Factory 多模态微调实践：微调 Qwen2-VL 构建文旅大模型](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)（中文）\n- [LLaMA Factory：微调LLaMA3模型实现角色扮演](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)（中文）\n\n
\n\n## 更新日志\n\n[25/07/02] 我们支持了 **[GLM-4.1V-9B-Thinking](https://github.com/THUDM/GLM-4.1V-Thinking)** 模型的微调。请安装 transformers 的 main 分支版本以使用。\n\n[25/04/28] 我们支持了 **[Qwen3](https://qwenlm.github.io/blog/qwen3/)** 系列模型的微调。\n\n[25/04/21] 我们支持了 **[Muon](https://github.com/KellerJordan/Muon)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。感谢 [@tianshijing](https://github.com/tianshijing) 的 PR。\n\n[25/04/16] 我们支持了 **[InternVL3](https://huggingface.co/OpenGVLab/InternVL3-8B)** 模型的微调。查看 [PR #7258](https://github.com/hiyouga/LLaMA-Factory/pull/7258) 以使用。\n\n[25/04/14] 我们支持了 **[GLM-Z1](https://huggingface.co/THUDM/GLM-Z1-9B-0414)** 和 **[Kimi-VL](https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct)** 模型的微调。\n\n[25/04/06] 我们支持了 **[Llama 4](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)** 模型的微调。查看 [PR #7611](https://github.com/hiyouga/LLaMA-Factory/pull/7611) 以使用。\n\n
展开日志\n\n[25/03/31] 我们支持了 **[Qwen2.5 Omni](https://qwenlm.github.io/blog/qwen2.5-omni/)** 模型的微调。查看 [PR #7537](https://github.com/hiyouga/LLaMA-Factory/pull/7537) 以使用。\n\n[25/03/15] 我们支持了 **[SGLang](https://github.com/sgl-project/sglang)** 推理后端，请使用 `infer_backend: sglang` 启用。\n\n[25/03/12] 我们支持了 **[Gemma 3](https://huggingface.co/blog/gemma3)** 模型的微调。\n\n[25/02/24] 我们宣布开源 **[EasyR1](https://github.com/hiyouga/EasyR1)**，一个高效可扩展的多模态强化学习框架，支持高效的 GRPO 训练。\n\n[25/02/11] 我们支持了在导出模型时保存 **[Ollama](https://github.com/ollama/ollama)** 配置文件。详细用法请参照 [examples](examples/README_zh.md)。\n\n[25/02/05] 我们支持了在语音理解任务上微调 **[Qwen2-Audio](Qwen/Qwen2-Audio-7B-Instruct)** 和 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 模型。\n\n[25/01/31] 我们支持了 **[DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1)** 和 **[Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)** 模型的微调。\n\n[25/01/15] 我们支持了 **[APOLLO](https://arxiv.org/abs/2412.05270)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。\n\n[25/01/14] 我们支持了 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 和 **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** 模型的微调。 感谢 [@BUAADreamer](https://github.com/BUAADreamer) 的 PR.\n\n[25/01/14] 我们支持了 **[InternLM 3](https://huggingface.co/collections/internlm/)** 模型的微调。感谢 [@hhaAndroid](https://github.com/hhaAndroid) 的 PR。\n\n[25/01/10] 我们支持了 **[Phi-4](https://huggingface.co/microsoft/phi-4)** 模型的微调。\n\n[24/12/21] 我们支持了使用 **[SwanLab](https://github.com/SwanHubX/SwanLab)** 跟踪与可视化实验。详细用法请参考 [此部分](#使用-swanlab-面板)。\n\n[24/11/27] 我们支持了 **[Skywork-o1](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)** 模型的微调和 **[OpenO1](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)** 数据集。\n\n[24/10/09] 我们支持了从 **[魔乐社区](https://modelers.cn/models)** 下载预训练模型和数据集。详细用法请参照 [此教程](#从魔乐社区下载)。\n\n[24/09/19] 我们支持了 **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** 模型的微调。\n\n[24/08/30] 我们支持了 **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** 模型的微调。感谢 [@simonJJJ](https://github.com/simonJJJ) 的 PR。\n\n[24/08/27] 我们支持了 **[Liger Kernel](https://github.com/linkedin/Liger-Kernel)**。请使用 `enable_liger_kernel: true` 来加速训练。\n\n[24/08/09] 我们支持了 **[Adam-mini](https://github.com/zyushun/Adam-mini)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。感谢 [@relic-yuexi](https://github.com/relic-yuexi) 的 PR。\n\n[24/07/04] 我们支持了[无污染打包训练](https://github.com/MeetKai/functionary/tree/main/functionary/train/packing)。请使用 `neat_packing: true` 参数。感谢 [@chuan298](https://github.com/chuan298) 的 PR。\n\n[24/06/16] 我们支持了 **[PiSSA](https://arxiv.org/abs/2404.02948)** 算法。详细用法请参照 [examples](examples/README_zh.md)。\n\n[24/06/07] 我们支持了 **[Qwen2](https://qwenlm.github.io/blog/qwen2/)** 和 **[GLM-4](https://github.com/THUDM/GLM-4)** 模型的微调。\n\n[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)。\n\n[24/05/20] 我们支持了 **PaliGemma** 系列模型的微调。注意 PaliGemma 是预训练模型，你需要使用 `paligemma` 模板进行微调使其获得对话能力。\n\n[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)。\n\n[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。\n\n[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)。\n\n[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)。\n\n[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)。\n\n[24/04/16] 我们支持了 **[BAdam](https://arxiv.org/abs/2404.02827)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。\n\n[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)。\n\n[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)。\n\n[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！\n\n[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)。\n\n[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)。\n\n[24/03/07] 我们支持了 **[GaLore](https://arxiv.org/abs/2403.03507)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。\n\n[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。\n\n[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。\n\n[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)。\n\n[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/)。\n\n[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。\n\n[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)。\n\n[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)。\n\n[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)。\n\n[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。\n\n[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。\n\n[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)。\n\n[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。\n\n[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。\n\n[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)。\n\n[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。\n\n[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)）。\n\n[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。\n\n[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。\n\n[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)。\n\n[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。\n\n[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)。\n\n
\n\n> [!TIP]\n> 如果您无法使用最新的功能，请尝试重新拉取代码并再次安装 LLaMA-Factory。\n\n## 模型\n\n| 模型名 | 参数量 | Template |\n| ----------------------------------------------------------------- | -------------------------------- | ------------------- |\n| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |\n| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |\n| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |\n| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |\n| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |\n| [DeepSeek 2.5/3](https://huggingface.co/deepseek-ai) | 236B/671B | deepseek3 |\n| [DeepSeek R1 (Distill)](https://huggingface.co/deepseek-ai) | 1.5B/7B/8B/14B/32B/70B/671B | deepseekr1 |\n| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |\n| [Falcon-H1](https://huggingface.co/tiiuae) | 0.5B/1.5B/3B/7B/34B | falcon_h1 |\n| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma/gemma2 |\n| [Gemma 3/Gemma 3n](https://huggingface.co/google) | 1B/4B/6B/8B/12B/27B | gemma3/gemma3n |\n| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM) | 9B/32B | glm4/glmz1 |\n| [GLM-4.1V](https://huggingface.co/THUDM)* | 9B | glm4v |\n| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |\n| [Granite 3.0-3.3](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |\n| [Granite 4](https://huggingface.co/ibm-granite) | 7B | granite4 |\n| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |\n| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |\n| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |\n| [InternVL 2.5-3](https://huggingface.co/OpenGVLab) | 1B/2B/8B/14B/38B/78B | intern_vl |\n| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |\n| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |\n| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |\n| [Llama 3-3.3](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |\n| [Llama 4](https://huggingface.co/meta-llama) | 109B/402B | llama4 |\n| [Llama 3.2 Vision](https://huggingface.co/meta-llama) | 11B/90B | mllama |\n| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |\n| [LLaVA-NeXT](https://huggingface.co/llava-hf) | 7B/8B/13B/34B/72B/110B | llava_next |\n| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf) | 7B/34B | llava_next_video |\n| [MiMo](https://huggingface.co/XiaomiMiMo) | 7B | mimo |\n| [MiniCPM](https://huggingface.co/openbmb) | 0.5B/1B/2B/4B/8B | cpm/cpm3/cpm4 |\n| [MiniCPM-o-2.6/MiniCPM-V-2.6](https://huggingface.co/openbmb) | 8B | minicpm_o/minicpm_v |\n| [Ministral/Mistral-Nemo](https://huggingface.co/mistralai) | 8B/12B | ministral |\n| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |\n| [Mistral Small](https://huggingface.co/mistralai) | 24B | mistral_small |\n| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |\n| [PaliGemma/PaliGemma2](https://huggingface.co/google) | 3B/10B/28B | paligemma |\n| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |\n| [Phi-3/Phi-3.5](https://huggingface.co/microsoft) | 4B/14B | phi |\n| [Phi-3-small](https://huggingface.co/microsoft) | 7B | phi_small |\n| [Phi-4](https://huggingface.co/microsoft) | 14B | phi4 |\n| [Pixtral](https://huggingface.co/mistralai) | 12B | pixtral |\n| [Qwen (1-2.5) (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |\n| [Qwen3 (MoE)](https://huggingface.co/Qwen) | 0.6B/1.7B/4B/8B/14B/32B/235B | qwen3 |\n| [Qwen2-Audio](https://huggingface.co/Qwen) | 7B | qwen2_audio |\n| [Qwen2.5-Omni](https://huggingface.co/Qwen) | 3B/7B | qwen2_omni |\n| [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen) | 2B/3B/7B/32B/72B | qwen2_vl |\n| [Seed Coder](https://huggingface.co/ByteDance-Seed) | 8B | seed_coder |\n| [Skywork o1](https://huggingface.co/Skywork) | 8B | skywork_o1 |\n| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |\n| [TeleChat2](https://huggingface.co/Tele-AI) | 3B/7B/35B/115B | telechat2 |\n| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |\n| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/6B/9B/34B | yi |\n| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |\n| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |\n\n> [!NOTE]\n> 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**。\n>\n> 请务必在训练和推理时采用**完全一致**的模板。\n>\n> \\*：您需要从 main 分支安装 `transformers` 并使用 `DISABLE_VERSION_CHECK=1` 来跳过版本检查。\n>\n> \\*\\*：您需要安装特定版本的 `transformers` 以使用该模型。\n\n项目所支持模型的完整列表请参阅 [constants.py](src/llamafactory/extras/constants.py)。\n\n您也可以在 [template.py](src/llamafactory/data/template.py) 中添加自己的对话模板。\n\n## 训练方法\n\n| 方法 | 全参数训练 | 部分参数训练 | LoRA | QLoRA |\n| --------------------- | ------------------ | ------------------ | ------------------ | ------------------ |\n| 预训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |\n| 指令监督微调 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |\n| 奖励模型训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |\n| PPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |\n| DPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |\n| KTO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |\n| ORPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |\n| SimPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |\n\n> [!TIP]\n> 有关 PPO 的实现细节，请参考[此博客](https://newfacade.github.io/notes-on-reinforcement-learning/17-ppo-trl.html)。\n\n## 数据集\n\n
预训练数据集\n\n- [Wiki Demo (en)](data/wiki_demo.txt)\n- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)\n- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)\n- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)\n- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)\n- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)\n- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)\n- [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)\n- [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)\n- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)\n- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)\n\n
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指令微调数据集\n\n- [Identity (en&zh)](data/identity.json)\n- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)\n- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)\n- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)\n- [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)\n- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)\n- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)\n- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)\n- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)\n- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)\n- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)\n- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)\n- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)\n- [UltraChat (en)](https://github.com/thunlp/UltraChat)\n- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)\n- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)\n- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)\n- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)\n- [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca)\n- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)\n- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)\n- [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa)\n- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)\n- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)\n- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)\n- [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)\n- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)\n- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)\n- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)\n- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)\n- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)\n- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)\n- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)\n- [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)\n- [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)\n- [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)\n- [Neo-sft (zh)](https://huggingface.co/datasets/m-a-p/neo_sft_phase2)\n- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)\n- [Magpie-ultra-v0.1 (en)](https://huggingface.co/datasets/argilla/magpie-ultra-v0.1)\n- [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)\n- [OpenO1-SFT (en&zh)](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)\n- [Open-Thoughts (en)](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k)\n- [Open-R1-Math (en)](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k)\n- [Chinese-DeepSeek-R1-Distill (zh)](https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT)\n- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)\n- [Pokemon-gpt4o-captions (en&zh)](https://huggingface.co/datasets/jugg1024/pokemon-gpt4o-captions)\n- [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)\n- [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)\n- [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)\n- [OpenSchnabeltier (de)](https://huggingface.co/datasets/mayflowergmbh/openschnabeltier_de)\n- [Evol Instruct (de)](https://huggingface.co/datasets/mayflowergmbh/evol-instruct_de)\n- [Dolphin (de)](https://huggingface.co/datasets/mayflowergmbh/dolphin_de)\n- [Booksum (de)](https://huggingface.co/datasets/mayflowergmbh/booksum_de)\n- [Airoboros (de)](https://huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de)\n- [Ultrachat (de)](https://huggingface.co/datasets/mayflowergmbh/ultra-chat_de)\n\n
\n\n
偏好数据集\n\n- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)\n- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)\n- [COIG-P (zh)](https://huggingface.co/datasets/m-a-p/COIG-P)\n- [RLHF-V (en)](https://huggingface.co/datasets/openbmb/RLHF-V-Dataset)\n- [VLFeedback (en)](https://huggingface.co/datasets/Zhihui/VLFeedback)\n- [RLAIF-V (en)](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset)\n- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)\n- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)\n- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)\n- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)\n- [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)\n\n
\n\n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。\n\n```bash\npip install --upgrade huggingface_hub\nhuggingface-cli login\n```\n\n## 软硬件依赖\n\n| 必需项 | 至少 | 推荐 |\n| ------------ | ------- | --------- |\n| python | 3.9 | 3.10 |\n| torch | 2.0.0 | 2.6.0 |\n| torchvision | 0.15.0 | 0.21.0 |\n| transformers | 4.49.0 | 4.50.0 |\n| datasets | 2.16.0 | 3.2.0 |\n| accelerate | 0.34.0 | 1.2.1 |\n| peft | 0.14.0 | 0.15.1 |\n| trl | 0.8.6 | 0.9.6 |\n\n| 可选项 | 至少 | 推荐 |\n| ------------ | ------- | --------- |\n| CUDA | 11.6 | 12.2 |\n| deepspeed | 0.10.0 | 0.16.4 |\n| bitsandbytes | 0.39.0 | 0.43.1 |\n| vllm | 0.4.3 | 0.8.2 |\n| flash-attn | 2.5.6 | 2.7.2 |\n\n### 硬件依赖\n\n\\* *估算值*\n\n| 方法 | 精度 | 7B | 14B | 30B | 70B | `x`B |\n| ------------------------------- | ---- | ----- | ----- | ----- | ------ | ------- |\n| Full (`bf16` or `fp16`) | 32 | 120GB | 240GB | 600GB | 1200GB | `18x`GB |\n| Full (`pure_bf16`) | 16 | 60GB | 120GB | 300GB | 600GB | `8x`GB |\n| Freeze/LoRA/GaLore/APOLLO/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | `2x`GB |\n| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | `x`GB |\n| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | `x/2`GB |\n| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | `x/4`GB |\n\n## 如何使用\n\n### 安装 LLaMA Factory\n\n> [!IMPORTANT]\n> 此步骤为必需。\n\n#### 从源码安装\n\n```bash\ngit clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git\ncd LLaMA-Factory\npip install -e \".[torch,metrics]\" --no-build-isolation\n```\n\n可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、aqlm、vllm、sglang、galore、apollo、badam、adam-mini、qwen、minicpm_v、openmind、swanlab、dev\n\n#### 从镜像安装\n\n```bash\ndocker run -it --rm --gpus=all --ipc=host hiyouga/llamafactory:latest\n```\n\n该镜像基于 Ubuntu 22.04（x86\\_64）、CUDA 12.4、Python 3.11、PyTorch 2.6.0 和 Flash-attn 2.7.4 构建。\n\n查看全部镜像：https://hub.docker.com/r/hiyouga/llamafactory/tags\n\n请参阅[构建 Docker](#构建-docker) 来重新构建镜像。\n\n
使用 uv 构建虚拟环境\n\n使用 [uv](https://github.com/astral-sh/uv) 创建隔离的 Python 环境：\n\n```bash\nuv sync --extra torch --extra metrics --prerelease=allow\n```\n\n在环境中运行 LLaMA-Factory：\n\n```bash\nuv run --prerelease=allow llamafactory-cli train examples/train_lora/llama3_lora_pretrain.yaml\n```\n\n
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Windows 用户指南\n\n#### 安装 PyTorch\n\nWindows 平台需要额外手动安装 GPU 版本的 PyTorch 依赖包，您可以参考[官方网站](https://pytorch.org/get-started/locally/)和以下命令安装并测试 PyTorch 是否正确安装。\n\n```bash\npip uninstall torch torchvision torchaudio\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126\npython -c \"import torch; print(torch.cuda.is_available())\"\n```\n\n如果看到 `True` 则说明安装成功。\n\n若遇到类似 `Can't pickle local object` 的报错，请设置 `dataloader_num_workers: 0`。\n\n#### 安装 BitsAndBytes\n\n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)。\n\n```bash\npip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl\n```\n\n#### 安装 Flash Attention-2\n\n如果要在 Windows 平台上开启 FlashAttention-2，请使用 [flash-attention-windows-wheel](https://huggingface.co/lldacing/flash-attention-windows-wheel) 中的脚本自行编译与安装。\n\n
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昇腾 NPU 用户指南\n\n在昇腾 NPU 设备上安装 LLaMA Factory 时，请升级 Python 到 3.10 及以上，并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 与 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：\n\n```bash\n# 请替换 URL 为 CANN 版本和设备型号对应的 URL\n# 安装 CANN Toolkit\nwget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-toolkit_8.0.RC1.alpha001_linux-\"$(uname -i)\".run\nbash Ascend-cann-toolkit_8.0.RC1.alpha001_linux-\"$(uname -i)\".run --install\n\n# 安装 CANN Kernels\nwget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run\nbash Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run --install\n\n# 设置环境变量\nsource /usr/local/Ascend/ascend-toolkit/set_env.sh\n```\n\n| 依赖项 | 至少 | 推荐 |\n| ------------ | ------- | -------------- |\n| CANN | 8.0.RC1 | 8.0.0.alpha002 |\n| torch | 2.1.0 | 2.4.0 |\n| torch-npu | 2.1.0 | 2.4.0.post2 |\n| deepspeed | 0.13.2 | 0.13.2 |\n| vllm-ascend | - | 0.7.3 |\n\n请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。\n\n如果遇到无法正常推理的情况，请尝试设置 `do_sample: false`。\n\n下载预构建 Docker 镜像：[32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)\n\n#### 安装 BitsAndBytes\n\n如果要在 Ascend NPU 上进行基于 bitsandbytes 的 QLoRA 量化微调，请执行如下步骤：\n\n1. 手动编译 bitsandbytes：请参考[安装文档](https://huggingface.co/docs/bitsandbytes/installation?backend=Ascend+NPU&platform=Ascend+NPU)完成 NPU 版的 bitsandbytes 安装，编译要求环境 cmake 版本不低于 3.22.1，g++ 版本不低于 12.x。\n\n```bash\n# 从源码安装 bitsandbytes\n# 克隆 bitsandbytes 仓库, Ascend NPU 目前在 multi-backend-refactor 中支持\ngit clone -b multi-backend-refactor https://github.com/bitsandbytes-foundation/bitsandbytes.git\ncd bitsandbytes/\n\n# 安装依赖\npip install -r requirements-dev.txt\n\n# 安装编译工具依赖，该步骤在不同系统上命令有所不同，供参考\napt-get install -y build-essential cmake\n\n# 编译 & 安装\ncmake -DCOMPUTE_BACKEND=npu -S .\nmake\npip install .\n```\n\n2. 安装 transformers 的 main 分支版本。\n\n```bash\ngit clone -b main https://github.com/huggingface/transformers.git\ncd transformers\npip install .\n```\n\n3. 在训练参数中设置 `double_quantization: false`，可参考[示例](examples/train_qlora/llama3_lora_sft_bnb_npu.yaml)。\n\n
\n\n### 数据准备\n\n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 / Modelers 上的数据集或加载本地数据集。\n\n> [!NOTE]\n> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。\n\n您也可以使用 **[Easy Dataset](https://github.com/ConardLi/easy-dataset)**、**[DataFlow](https://github.com/OpenDCAI/DataFlow)** 和 **[GraphGen](https://github.com/open-sciencelab/GraphGen)** 构建用于微调的合成数据。\n\n### 快速开始\n\n下面三行命令分别对 Llama3-8B-Instruct 模型进行 LoRA **微调**、**推理**和**合并**。\n\n```bash\nllamafactory-cli train examples/train_lora/llama3_lora_sft.yaml\nllamafactory-cli chat examples/inference/llama3_lora_sft.yaml\nllamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml\n```\n\n高级用法请参考 [examples/README_zh.md](examples/README_zh.md)（包括多 GPU 微调）。\n\n> [!TIP]\n> 使用 `llamafactory-cli help` 显示帮助信息。\n>\n> 遇到报错请先看[常见问题](https://github.com/hiyouga/LLaMA-Factory/issues/4614)。\n\n### LLaMA Board 可视化微调（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）\n\n```bash\nllamafactory-cli webui\n```\n\n### 构建 Docker\n\nCUDA 用户：\n\n```bash\ncd docker/docker-cuda/\ndocker compose up -d\ndocker compose exec llamafactory bash\n```\n\n昇腾 NPU 用户：\n\n```bash\ncd docker/docker-npu/\ndocker compose up -d\ndocker compose exec llamafactory bash\n```\n\nAMD ROCm 用户：\n\n```bash\ncd docker/docker-rocm/\ndocker compose up -d\ndocker compose exec llamafactory bash\n```\n\n
不使用 Docker Compose 构建\n\nCUDA 用户：\n\n```bash\ndocker build -f ./docker/docker-cuda/Dockerfile \\\n --build-arg PIP_INDEX=https://pypi.org/simple \\\n --build-arg EXTRAS=metrics \\\n -t llamafactory:latest .\n\ndocker run -dit --ipc=host --gpus=all \\\n -p 7860:7860 \\\n -p 8000:8000 \\\n --name llamafactory \\\n llamafactory:latest\n\ndocker exec -it llamafactory bash\n```\n\n昇腾 NPU 用户：\n\n```bash\ndocker build -f ./docker/docker-npu/Dockerfile \\\n --build-arg PIP_INDEX=https://pypi.org/simple \\\n --build-arg EXTRAS=torch-npu,metrics \\\n -t llamafactory:latest .\n\ndocker run -dit --ipc=host \\\n -v /usr/local/dcmi:/usr/local/dcmi \\\n -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \\\n -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \\\n -v /etc/ascend_install.info:/etc/ascend_install.info \\\n -p 7860:7860 \\\n -p 8000:8000 \\\n --device /dev/davinci0 \\\n --device /dev/davinci_manager \\\n --device /dev/devmm_svm \\\n --device /dev/hisi_hdc \\\n --name llamafactory \\\n llamafactory:latest\n\ndocker exec -it llamafactory bash\n```\n\nAMD ROCm 用户：\n\n```bash\ndocker build -f ./docker/docker-rocm/Dockerfile \\\n --build-arg PIP_INDEX=https://pypi.org/simple \\\n --build-arg EXTRAS=metrics \\\n -t llamafactory:latest .\n\ndocker run -dit --ipc=host \\\n -p 7860:7860 \\\n -p 8000:8000 \\\n --device /dev/kfd \\\n --device /dev/dri \\\n --name llamafactory \\\n llamafactory:latest\n\ndocker exec -it llamafactory bash\n```\n\n
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使用数据卷\n\n您可以通过移除 Dockerfile 中 `VOLUME [ \"/root/.cache/huggingface\", \"/app/shared_data\", \"/app/output\" ]` 的注释来使用数据卷。\n\n在构建 Docker 时使用参数 `-v ./hf_cache:/root/.cache/huggingface` 来挂载数据卷。各个数据卷的含义表示如下。\n\n- `hf_cache`：使用宿主机的 Hugging Face 缓存文件夹。\n- `shared_data`：宿主机中存放数据集的文件夹路径。\n- `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。\n\n
\n\n### 利用 vLLM 部署 OpenAI API\n\n```bash\nAPI_PORT=8000 llamafactory-cli api examples/inference/llama3.yaml infer_backend=vllm vllm_enforce_eager=true\n```\n\n> [!TIP]\n> API 文档请查阅[这里](https://platform.openai.com/docs/api-reference/chat/create)。\n>\n> 示例：[图像理解](scripts/api_example/test_image.py) | [工具调用](scripts/api_example/test_toolcall.py)\n\n### 从魔搭社区下载\n\n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。\n\n```bash\nexport USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`\n```\n\n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`。\n\n### 从魔乐社区下载\n\n您也可以通过下述方法，使用魔乐社区下载数据集和模型。\n\n```bash\nexport USE_OPENMIND_HUB=1 # Windows 使用 `set USE_OPENMIND_HUB=1`\n```\n\n将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔乐社区](https://modelers.cn/models)查看所有可用的模型，例如 `TeleAI/TeleChat-7B-pt`。\n\n### 使用 W&B 面板\n\n若要使用 [Weights & Biases](https://wandb.ai) 记录实验数据，请在 yaml 文件中添加下面的参数。\n\n```yaml\nreport_to: wandb\nrun_name: test_run # 可选\n```\n\n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。\n\n### 使用 SwanLab 面板\n\n若要使用 [SwanLab](https://github.com/SwanHubX/SwanLab) 记录实验数据，请在 yaml 文件中添加下面的参数。\n\n```yaml\nuse_swanlab: true\nswanlab_run_name: test_run # 可选\n```\n\n在启动训练任务时，登录SwanLab账户有以下三种方式：\n\n方式一：在 yaml 文件中添加 `swanlab_api_key=` ，并设置为你的 [API 密钥](https://swanlab.cn/settings)。\n方式二：将环境变量 `SWANLAB_API_KEY` 设置为你的 [API 密钥](https://swanlab.cn/settings)。\n方式三：启动前使用 `swanlab login` 命令完成登录。\n\n## 使用了 LLaMA Factory 的项目\n\n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。\n\n
点击显示\n\n1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)\n1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)\n1. 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)\n1. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [[arxiv]](https://arxiv.org/abs/2311.07816)\n1. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [[arxiv]](https://arxiv.org/abs/2312.15710)\n1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. KDD 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)\n1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)\n1. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2402.05904)\n1. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [[arxiv]](https://arxiv.org/abs/2402.07625)\n1. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11176)\n1. Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [[arxiv]](https://arxiv.org/abs/2402.11187)\n1. 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)\n1. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11801)\n1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2402.11809)\n1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819)\n1. 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)\n1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714)\n1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)\n1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)\n1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)\n1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)\n1. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2403.09073)\n1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)\n1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)\n1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. COLING 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)\n1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)\n1. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2404.00604)\n1. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.02827)\n1. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2404.04167)\n1. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. ICML 2024. [[arxiv]](https://arxiv.org/abs/2404.04316)\n1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)\n1. 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)\n1. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.11581)\n1. 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)\n1. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2404.16621)\n1. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2404.17140)\n1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)\n1. Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [[arxiv]](https://arxiv.org/abs/2405.04760)\n1. Dammu et al. \"They are uncultured\": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [[arxiv]](https://arxiv.org/abs/2405.05378)\n1. Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [[arxiv]](https://arxiv.org/abs/2405.09055)\n1. Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [[arxiv]](https://arxiv.org/abs/2405.12739)\n1. Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2405.13816)\n1. Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2405.20215)\n1. Zihong Chen. 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[[paper]](https://dl.acm.org/doi/10.1145/3627673.3679611)\n1. **[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。\n1. **[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。\n1. **[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。\n1. **[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。\n1. **[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。\n1. **[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)\n1. **[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。\n1. **[AutoRE](https://github.com/THUDM/AutoRE)**：基于大语言模型的文档级关系抽取系统。\n1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**：在 Windows 主机上利用英伟达 RTX 设备进行大型语言模型微调的开发包。\n1. **[LazyLLM](https://github.com/LazyAGI/LazyLLM)**：一个低代码构建多 Agent 大模型应用的开发工具，支持基于 LLaMA Factory 的模型微调.\n1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**：一个全链路 RAG 检索模型微调、推理和蒸馏代码库。[[blog]](https://zhuanlan.zhihu.com/p/987727357)\n1. **[360-LLaMA-Factory](https://github.com/Qihoo360/360-LLaMA-Factory)**：一个魔改后的代码库，通过 Ring Attention 支持长序列的 SFT 和 DPO 训练。\n1. **[Sky-T1](https://novasky-ai.github.io/posts/sky-t1/)**：由 NovaSky AI 微调的低成本类 o1 长推理模型。\n1. **[WeClone](https://github.com/xming521/WeClone)**：从聊天记录创造数字分身的一站式解决方案。\n\n
\n\n## 协议\n\n本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。\n\n使用模型权重时，请遵循对应的模型协议：[Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [GPT-2](https://github.com/openai/gpt-2/blob/master/LICENSE) / [Granite](LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM](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/) / [Llama 3](https://llama.meta.com/llama3/license/) / [Llama 4](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3/Phi-4](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [Skywork](https://huggingface.co/Skywork/Skywork-13B-base/blob/main/Skywork%20Community%20License.pdf) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [TeleChat2](https://huggingface.co/Tele-AI/telechat-7B/blob/main/TeleChat%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)\n\n## 引用\n\n如果您觉得此项目有帮助，请考虑以下列格式引用\n\n```bibtex\n@inproceedings{zheng2024llamafactory,\n title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},\n author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Zhangchi Feng and Yongqiang Ma},\n booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},\n address={Bangkok, Thailand},\n publisher={Association for Computational Linguistics},\n year={2024},\n url={http://arxiv.org/abs/2403.13372}\n}\n```\n\n## 致谢\n\n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。\n\n## Star History\n\n![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date)\n"}}}}