685 lines
44 KiB
Markdown
685 lines
44 KiB
Markdown
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[](https://github.com/hiyouga/LLaMA-Factory/stargazers)
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[](LICENSE)
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[](https://github.com/hiyouga/LLaMA-Factory/commits/main)
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[](https://pypi.org/project/llamafactory/)
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[](#使用了-llama-factory-的项目)
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[](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[](https://discord.gg/rKfvV9r9FK)
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[](https://twitter.com/llamafactory_ai)
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[](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)
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[](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
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[](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
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[](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
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[](https://trendshift.io/repositories/4535)
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👋 加入我们的[微信群](assets/wechat.jpg)或 [NPU 用户群](assets/wechat_npu.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/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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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)
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- [更新日志](#更新日志)
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- [模型](#模型)
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- [训练方法](#训练方法)
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- [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)
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- [使用了 LLaMA Factory 的项目](#使用了-llama-factory-的项目)
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- [协议](#协议)
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- [引用](#引用)
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- [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**：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。
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- **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 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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<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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[24/06/16] 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)。
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[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。
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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 |
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| ------------------------------------------------------------ | -------------------------------- | --------- |
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| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
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| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
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| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
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| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
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| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
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| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
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| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
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| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
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| [InternLM2/InternLM2.5](https://huggingface.co/internlm) | 7B/20B | intern2 |
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
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| [Llama 3/Llama 3.1](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
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| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | vicuna |
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| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
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| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
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| [PaliGemma](https://huggingface.co/google) | 3B | gemma |
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| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
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| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
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| [Qwen/Qwen1.5/Qwen2 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen |
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| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
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| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
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| [Yi/Yi-1.5](https://huggingface.co/01-ai) | 6B/9B/34B | yi |
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| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
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| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
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| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B | cpm |
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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 |
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| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
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| 预训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| 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: |
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| PPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| 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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> [!TIP]
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> 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)。
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## 数据集
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<details><summary>预训练数据集</summary>
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- [Wiki Demo (en)](data/wiki_demo.txt)
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- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
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- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)
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- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
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- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
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- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
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- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
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- [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
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- [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
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- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
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- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
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</details>
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<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)
|
||
- [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
|
||
- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
|
||
- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
|
||
- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
|
||
- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
|
||
- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
|
||
- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
|
||
- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
|
||
- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
|
||
- [UltraChat (en)](https://github.com/thunlp/UltraChat)
|
||
- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
|
||
- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
|
||
- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
|
||
- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
|
||
- [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca)
|
||
- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
|
||
- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
|
||
- [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa)
|
||
- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
|
||
- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
|
||
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
|
||
- [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
|
||
- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
|
||
- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
|
||
- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
|
||
- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
|
||
- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
|
||
- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
|
||
- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
|
||
- [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
|
||
- [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
|
||
- [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
|
||
- [Neo-sft (zh)](https://huggingface.co/datasets/m-a-p/neo_sft_phase2)
|
||
- [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)
|
||
- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)
|
||
- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
|
||
- [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
|
||
- [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
|
||
- [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)
|
||
- [OpenSchnabeltier (de)](https://huggingface.co/datasets/mayflowergmbh/openschnabeltier_de)
|
||
- [Evol Instruct (de)](https://huggingface.co/datasets/mayflowergmbh/evol-instruct_de)
|
||
- [Dolphin (de)](https://huggingface.co/datasets/mayflowergmbh/dolphin_de)
|
||
- [Booksum (de)](https://huggingface.co/datasets/mayflowergmbh/booksum_de)
|
||
- [Airoboros (de)](https://huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de)
|
||
- [Ultrachat (de)](https://huggingface.co/datasets/mayflowergmbh/ultra-chat_de)
|
||
|
||
</details>
|
||
|
||
<details><summary>偏好数据集</summary>
|
||
|
||
- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
|
||
- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
|
||
- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
|
||
- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
|
||
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
|
||
- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
|
||
- [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)
|
||
|
||
</details>
|
||
|
||
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。
|
||
|
||
```bash
|
||
pip install --upgrade huggingface_hub
|
||
huggingface-cli login
|
||
```
|
||
|
||
## 软硬件依赖
|
||
|
||
| 必需项 | 至少 | 推荐 |
|
||
| ------------ | ------- | --------- |
|
||
| 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 |
|
||
| trl | 0.8.6 | 0.9.4 |
|
||
|
||
| 可选项 | 至少 | 推荐 |
|
||
| ------------ | ------- | --------- |
|
||
| CUDA | 11.6 | 12.2 |
|
||
| deepspeed | 0.10.0 | 0.14.0 |
|
||
| bitsandbytes | 0.39.0 | 0.43.1 |
|
||
| vllm | 0.4.3 | 0.4.3 |
|
||
| flash-attn | 2.3.0 | 2.5.9 |
|
||
|
||
### 硬件依赖
|
||
|
||
\* *估算值*
|
||
|
||
| 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 |
|
||
|
||
## 如何使用
|
||
|
||
### 安装 LLaMA Factory
|
||
|
||
> [!IMPORTANT]
|
||
> 此步骤为必需。
|
||
|
||
```bash
|
||
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
|
||
cd LLaMA-Factory
|
||
pip install -e ".[torch,metrics]"
|
||
```
|
||
|
||
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、qwen、modelscope、quality
|
||
|
||
> [!TIP]
|
||
> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
|
||
|
||
<details><summary>Windows 用户指南</summary>
|
||
|
||
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)。
|
||
|
||
```bash
|
||
pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
|
||
```
|
||
|
||
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。
|
||
|
||
</details>
|
||
|
||
<details><summary>昇腾 NPU 用户指南</summary>
|
||
|
||
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 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：
|
||
|
||
```bash
|
||
# 请替换 URL 为 CANN 版本和设备型号对应的 URL
|
||
# 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-"$(uname -i)".run
|
||
bash Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run --install
|
||
|
||
# 安装 CANN Kernels
|
||
wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run
|
||
bash Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run --install
|
||
|
||
# 设置环境变量
|
||
source /usr/local/Ascend/ascend-toolkit/set_env.sh
|
||
```
|
||
|
||
| 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 |
|
||
|
||
请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。
|
||
|
||
如果遇到无法正常推理的情况，请尝试设置 `do_sample: false`。
|
||
|
||
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)
|
||
|
||
</details>
|
||
|
||
### 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
|
||
|
||
下面三行命令分别对 Llama3-8B-Instruct 模型进行 LoRA **微调**、**推理**和**合并**。
|
||
|
||
```bash
|
||
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
|
||
llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
|
||
llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
|
||
```
|
||
|
||
高级用法请参考 [examples/README_zh.md](examples/README_zh.md)（包括多 GPU 微调）。
|
||
|
||
> [!TIP]
|
||
> 使用 `llamafactory-cli help` 显示帮助信息。
|
||
|
||
### LLaMA Board 可视化微调（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）
|
||
|
||
```bash
|
||
llamafactory-cli webui
|
||
```
|
||
|
||
### 构建 Docker
|
||
|
||
CUDA 用户：
|
||
|
||
```bash
|
||
cd docker/docker-cuda/
|
||
docker compose up -d
|
||
docker compose exec llamafactory bash
|
||
```
|
||
|
||
昇腾 NPU 用户：
|
||
|
||
```bash
|
||
cd docker/docker-npu/
|
||
docker compose up -d
|
||
docker compose exec llamafactory bash
|
||
```
|
||
|
||
AMD ROCm 用户：
|
||
|
||
```bash
|
||
cd docker/docker-rocm/
|
||
docker compose up -d
|
||
docker compose exec llamafactory bash
|
||
```
|
||
|
||
<details><summary>不使用 Docker Compose 构建</summary>
|
||
|
||
CUDA 用户：
|
||
|
||
```bash
|
||
docker build -f ./docker/docker-cuda/Dockerfile \
|
||
--build-arg INSTALL_BNB=false \
|
||
--build-arg INSTALL_VLLM=false \
|
||
--build-arg INSTALL_DEEPSPEED=false \
|
||
--build-arg INSTALL_FLASHATTN=false \
|
||
--build-arg PIP_INDEX=https://pypi.org/simple \
|
||
-t llamafactory:latest .
|
||
|
||
docker run -dit --gpus=all \
|
||
-v ./hf_cache:/root/.cache/huggingface \
|
||
-v ./ms_cache:/root/.cache/modelscope \
|
||
-v ./data:/app/data \
|
||
-v ./output:/app/output \
|
||
-p 7860:7860 \
|
||
-p 8000:8000 \
|
||
--shm-size 16G \
|
||
--name llamafactory \
|
||
llamafactory:latest
|
||
|
||
docker exec -it llamafactory bash
|
||
```
|
||
|
||
昇腾 NPU 用户：
|
||
|
||
```bash
|
||
# 根据您的环境选择镜像
|
||
docker build -f ./docker/docker-npu/Dockerfile \
|
||
--build-arg INSTALL_DEEPSPEED=false \
|
||
--build-arg PIP_INDEX=https://pypi.org/simple \
|
||
-t llamafactory:latest .
|
||
|
||
# 根据您的资源更改 `device`
|
||
docker run -dit \
|
||
-v ./hf_cache:/root/.cache/huggingface \
|
||
-v ./ms_cache:/root/.cache/modelscope \
|
||
-v ./data:/app/data \
|
||
-v ./output:/app/output \
|
||
-v /usr/local/dcmi:/usr/local/dcmi \
|
||
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
|
||
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
|
||
-v /etc/ascend_install.info:/etc/ascend_install.info \
|
||
-p 7860:7860 \
|
||
-p 8000:8000 \
|
||
--device /dev/davinci0 \
|
||
--device /dev/davinci_manager \
|
||
--device /dev/devmm_svm \
|
||
--device /dev/hisi_hdc \
|
||
--shm-size 16G \
|
||
--name llamafactory \
|
||
llamafactory:latest
|
||
|
||
docker exec -it llamafactory bash
|
||
```
|
||
|
||
AMD ROCm 用户：
|
||
|
||
```bash
|
||
docker build -f ./docker/docker-rocm/Dockerfile \
|
||
--build-arg INSTALL_BNB=false \
|
||
--build-arg INSTALL_VLLM=false \
|
||
--build-arg INSTALL_DEEPSPEED=false \
|
||
--build-arg INSTALL_FLASHATTN=false \
|
||
--build-arg PIP_INDEX=https://pypi.org/simple \
|
||
-t llamafactory:latest .
|
||
|
||
docker run -dit \
|
||
-v ./hf_cache:/root/.cache/huggingface \
|
||
-v ./ms_cache:/root/.cache/modelscope \
|
||
-v ./data:/app/data \
|
||
-v ./output:/app/output \
|
||
-v ./saves:/app/saves \
|
||
-p 7860:7860 \
|
||
-p 8000:8000 \
|
||
--device /dev/kfd \
|
||
--device /dev/dri \
|
||
--shm-size 16G \
|
||
--name llamafactory \
|
||
llamafactory:latest
|
||
|
||
docker exec -it llamafactory bash
|
||
```
|
||
|
||
</details>
|
||
|
||
<details><summary>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。
|
||
|
||
</details>
|
||
|
||
### 利用 vLLM 部署 OpenAI API
|
||
|
||
```bash
|
||
API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
|
||
```
|
||
|
||
> [!TIP]
|
||
> API 文档请查阅 https://platform.openai.com/docs/api-reference/chat/create。
|
||
|
||
### 从魔搭社区下载
|
||
|
||
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`
|
||
```
|
||
|
||
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，请在 yaml 文件中添加下面的参数。
|
||
|
||
```yaml
|
||
report_to: wandb
|
||
run_name: test_run # 可选
|
||
```
|
||
|
||
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。
|
||
|
||
## 使用了 LLaMA Factory 的项目
|
||
|
||
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。
|
||
|
||
<details><summary>点击显示</summary>
|
||
|
||
1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
|
||
1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
|
||
1. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [[arxiv]](https://arxiv.org/abs/2308.10526)
|
||
1. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [[arxiv]](https://arxiv.org/abs/2311.07816)
|
||
1. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [[arxiv]](https://arxiv.org/abs/2312.15710)
|
||
1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. KDD 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
|
||
1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
|
||
1. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2402.05904)
|
||
1. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [[arxiv]](https://arxiv.org/abs/2402.07625)
|
||
1. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11176)
|
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1. Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [[arxiv]](https://arxiv.org/abs/2402.11187)
|
||
1. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [[arxiv]](https://arxiv.org/abs/2402.11746)
|
||
1. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11801)
|
||
1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2402.11809)
|
||
1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819)
|
||
1. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [[arxiv]](https://arxiv.org/abs/2402.12204)
|
||
1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714)
|
||
1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
|
||
1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)
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1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)
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1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)
|
||
1. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2403.09073)
|
||
1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
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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. COLING 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
|
||
1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)
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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. ICML 2024. [[arxiv]](https://arxiv.org/abs/2404.04316)
|
||
1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
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||
1. Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.09836)
|
||
1. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.11581)
|
||
1. Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [[arxiv]](https://arxiv.org/abs/2404.14215)
|
||
1. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2404.16621)
|
||
1. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2404.17140)
|
||
1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
|
||
1. Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [[arxiv]](https://arxiv.org/abs/2405.04760)
|
||
1. Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [[arxiv]](https://arxiv.org/abs/2405.05378)
|
||
1. Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [[arxiv]](https://arxiv.org/abs/2405.09055)
|
||
1. Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [[arxiv]](https://arxiv.org/abs/2405.12739)
|
||
1. Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2405.13816)
|
||
1. Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2405.20215)
|
||
1. Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [[paper]](https://aclanthology.org/2024.lt4hala-1.30)
|
||
1. Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2406.00380)
|
||
1. Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [[arxiv]](https://arxiv.org/abs/2406.02106)
|
||
1. Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [[arxiv]](https://arxiv.org/abs/2406.03136)
|
||
1. Ge et al. Time Sensitive Knowledge Editing through Efficient Finetuning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2406.04496)
|
||
1. Tan et al. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions. 2024. [[arxiv]](https://arxiv.org/abs/2406.05688)
|
||
1. Song et al. Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters. 2024. [[arxiv]](https://arxiv.org/abs/2406.05955)
|
||
1. Gu et al. RWKV-CLIP: A Robust Vision-Language Representation Learner. 2024. [[arxiv]](https://arxiv.org/abs/2406.06973)
|
||
1. Chen et al. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. 2024. [[arxiv]](https://arxiv.org/abs/2406.07115)
|
||
1. Zhu et al. Are Large Language Models Good Statisticians?. 2024. [[arxiv]](https://arxiv.org/abs/2406.07815)
|
||
1. Li et al. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2406.10099)
|
||
1. Ding et al. IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce. 2024. [[arxiv]](https://arxiv.org/abs/2406.10173)
|
||
1. He et al. COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities. 2024. [[arxiv]](https://arxiv.org/abs/2406.12074)
|
||
1. Lin et al. FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. 2024. [[arxiv]](https://arxiv.org/abs/2406.14408)
|
||
1. Treutlein et al. Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. 2024. [[arxiv]](https://arxiv.org/abs/2406.14546)
|
||
1. Feng et al. SS-Bench: A Benchmark for Social Story Generation and Evaluation. 2024. [[arxiv]](https://arxiv.org/abs/2406.15695)
|
||
1. Feng et al. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. 2024. [[arxiv]](https://arxiv.org/abs/2406.17233)
|
||
1. Liu et al. Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals. 2024. [[arxiv]](https://arxiv.org/abs/2406.18069)
|
||
1. Iyer et al. Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh’s Submission to AmericasNLP 2024 Translation Task. AmericasNLP 2024. [[paper]](https://aclanthology.org/2024.americasnlp-1.25)
|
||
1. **[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/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。
|
||
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。
|
||
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)
|
||
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。
|
||
1. **[AutoRE](https://github.com/THUDM/AutoRE)**：基于大语言模型的文档级关系抽取系统。
|
||
1. **[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。
|
||
1. **[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.
|
||
|
||
</details>
|
||
|
||
## 协议
|
||
|
||
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。
|
||
|
||
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) / [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/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
|
||
|
||
## 引用
|
||
|
||
如果您觉得此项目有帮助，请考虑以下列格式引用
|
||
|
||
```bibtex
|
||
@inproceedings{zheng2024llamafactory,
|
||
title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
|
||
author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Zhangchi Feng and Yongqiang Ma},
|
||
booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
|
||
address={Bangkok, Thailand},
|
||
publisher={Association for Computational Linguistics},
|
||
year={2024},
|
||
url={http://arxiv.org/abs/2403.13372}
|
||
}
|
||
```
|
||
|
||
## 致谢
|
||
|
||
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。
|
||
|
||
## Star History
|
||
|
||
|