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	![# LLaMA Factory](assets/logo.png)

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	👋 加入我们的[微信群](assets/wechat.jpg)或 [NPU 用户群](assets/wechat_npu.jpg)。

	\[ [English](README.md) \| 中文 \]

	微调大模型可以像这样轻松…

	https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd-d76c6d0a6594

	选择你的打开方式：

	- Colab：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
	- PAI-DSW: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
	- 本地机器：请见[如何使用](#如何使用)

	## 目录

	- [项目特色](#项目特色)
	- [性能指标](#性能指标)
	- [更新日志](#更新日志)
	- [模型](#模型)
	- [训练方法](#训练方法)
	- [数据集](#数据集)
	- [软硬件依赖](#软硬件依赖)
	- [如何使用](#如何使用)
	- [使用了 LLaMA Factory 的项目](#使用了-llama-factory-的项目)
	- [协议](#协议)
	- [引用](#引用)
	- [致谢](#致谢)

	## 项目特色

	- 多种模型：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
	- 集成方法：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
	- 多种精度：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
	- 先进算法：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
	- 实用技巧：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
	- 实验监控：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
	- 极速推理：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。

	## 性能指标

	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。

	![benchmark](assets/benchmark.svg)

	<details><summary>变量定义</summary>

	- 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）
	- 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）
	- 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）
	- 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`。

	</details>

	## 更新日志

	[24/06/16] 我们支持了 [PiSSA](https://arxiv.org/abs/2404.02948) 算法。详细用法请参照 [examples](examples/README_zh.md)。

	[24/06/07] 我们支持了 [Qwen2](https://qwenlm.github.io/blog/qwen2/) 和 [GLM-4](https://github.com/THUDM/GLM-4) 模型的微调。

	[24/05/26] 我们支持了 [SimPO](https://arxiv.org/abs/2405.14734) 偏好对齐算法。详细用法请参照 [examples](examples/README_zh.md)。

	<details><summary>展开日志</summary>

	[24/05/20] 我们支持了 PaliGemma 系列模型的微调。注意 PaliGemma 是预训练模型，你需要使用 `gemma` 模板进行微调使其获得对话能力。

	[24/05/18] 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)。

	[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。

	[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)。

	[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)。

	[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)。

	[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)。

	[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)。

	[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)。

	[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！

	[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)。

	[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)。

	[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)。

	[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。

	[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。

	[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)。

	[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/)。

	[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。

	[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)。

	[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)。

	[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)。

	[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。

	[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。

	[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)。

	[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。

	[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。

	[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)。

	[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。

	[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)）。

	[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。

	[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。

	[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)。

	[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。

	[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)。

	</details>

	## 模型

	\| 模型名 \| 模型大小 \| Template \|
	\| ------------------------------------------------------------ \| -------------------------------- \| --------- \|
	\| [Baichuan 2](https://huggingface.co/baichuan-inc) \| 7B/13B \| baichuan2 \|
	\| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) \| 560M/1.1B/1.7B/3B/7.1B/176B \| - \|
	\| [ChatGLM3](https://huggingface.co/THUDM) \| 6B \| chatglm3 \|
	\| [Command R](https://huggingface.co/CohereForAI) \| 35B/104B \| cohere \|
	\| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) \| 7B/16B/67B/236B \| deepseek \|
	\| [Falcon](https://huggingface.co/tiiuae) \| 7B/11B/40B/180B \| falcon \|
	\| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) \| 2B/7B/9B/27B \| gemma \|
	\| [GLM-4](https://huggingface.co/THUDM) \| 9B \| glm4 \|
	\| [InternLM2](https://huggingface.co/internlm) \| 7B/20B \| intern2 \|
	\| [Llama](https://github.com/facebookresearch/llama) \| 7B/13B/33B/65B \| - \|
	\| [Llama 2](https://huggingface.co/meta-llama) \| 7B/13B/70B \| llama2 \|
	\| [Llama 3](https://huggingface.co/meta-llama) \| 8B/70B \| llama3 \|
	\| [LLaVA-1.5](https://huggingface.co/llava-hf) \| 7B/13B \| vicuna \|
	\| [Mistral/Mixtral](https://huggingface.co/mistralai) \| 7B/8x7B/8x22B \| mistral \|
	\| [OLMo](https://huggingface.co/allenai) \| 1B/7B \| - \|
	\| [PaliGemma](https://huggingface.co/google) \| 3B \| gemma \|
	\| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) \| 1.3B/2.7B \| - \|
	\| [Phi-3](https://huggingface.co/microsoft) \| 4B/7B/14B \| phi \|
	\| [Qwen/Qwen1.5/Qwen2 (Code/MoE)](https://huggingface.co/Qwen) \| 0.5B/1.5B/4B/7B/14B/32B/72B/110B \| qwen \|
	\| [StarCoder 2](https://huggingface.co/bigcode) \| 3B/7B/15B \| - \|
	\| [XVERSE](https://huggingface.co/xverse) \| 7B/13B/65B \| xverse \|
	\| [Yi/Yi-1.5](https://huggingface.co/01-ai) \| 6B/9B/34B \| yi \|
	\| [Yi-VL](https://huggingface.co/01-ai) \| 6B/34B \| yi_vl \|
	\| [Yuan 2](https://huggingface.co/IEITYuan) \| 2B/51B/102B \| yuan \|

	> [!NOTE]
	> 对于所有“基座”（Base）模型，`template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Instruct/Chat）模型请务必使用对应的模板。
	>
	> 请务必在训练和推理时采用完全一致的模板。

	项目所支持模型的完整列表请参阅 [constants.py](src/llamafactory/extras/constants.py)。

	您也可以在 [template.py](src/llamafactory/data/template.py) 中添加自己的对话模板。

	## 训练方法

	\| 方法 \| 全参数训练 \| 部分参数训练 \| LoRA \| QLoRA \|
	\| ---------------------- \| ------------------ \| ------------------ \| ------------------ \| ------------------ \|
	\| 预训练 \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| 指令监督微调 \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| 奖励模型训练 \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| PPO 训练 \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| DPO 训练 \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| KTO 训练 \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| ORPO 训练 \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| SimPO 训练 \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|

	## 数据集

	<details><summary>预训练数据集</summary>

	- [Wiki Demo (en)](data/wiki_demo.txt)
	- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
	- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)
	- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
	- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
	- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
	- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
	- [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
	- [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
	- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
	- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)

	</details>

	<details><summary>指令微调数据集</summary>

	- [Identity (en&zh)](data/identity.json)
	- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
	- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)
	- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
	- [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
	- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
	- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
	- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
	- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
	- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
	- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
	- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
	- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
	- [UltraChat (en)](https://github.com/thunlp/UltraChat)
	- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
	- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
	- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
	- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
	- [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca)
	- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
	- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
	- [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa)
	- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
	- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
	- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
	- [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
	- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
	- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
	- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
	- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
	- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
	- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
	- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
	- [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
	- [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
	- [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
	- [Neo-sft (zh)](https://huggingface.co/datasets/m-a-p/neo_sft_phase2)
	- [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)
	- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)
	- [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
	```

	<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
	```

	</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)
	1. Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [[arxiv]](https://arxiv.org/abs/2402.11187)
	1. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [[arxiv]](https://arxiv.org/abs/2402.11746)
	1. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11801)
	1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2402.11809)
	1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819)
	1. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [[arxiv]](https://arxiv.org/abs/2402.12204)
	1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714)
	1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
	1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)
	1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)
	1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)
	1. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2403.09073)
	1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
	1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)
	1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. COLING 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
	1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)
	1. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2404.00604)
	1. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.02827)
	1. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2404.04167)
	1. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. ICML 2024. [[arxiv]](https://arxiv.org/abs/2404.04316)
	1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
	1. Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.09836)
	1. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.11581)
	1. Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [[arxiv]](https://arxiv.org/abs/2404.14215)
	1. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2404.16621)
	1. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2404.17140)
	1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
	1. Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [[arxiv]](https://arxiv.org/abs/2405.04760)
	1. Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [[arxiv]](https://arxiv.org/abs/2405.05378)
	1. Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [[arxiv]](https://arxiv.org/abs/2405.09055)
	1. Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [[arxiv]](https://arxiv.org/abs/2405.12739)
	1. Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2405.13816)
	1. Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2405.20215)
	1. Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [[paper]](https://aclanthology.org/2024.lt4hala-1.30)
	1. Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2406.00380)
	1. Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [[arxiv]](https://arxiv.org/abs/2406.02106)
	1. Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [[arxiv]](https://arxiv.org/abs/2406.03136)
	1. Ge et al. Time Sensitive Knowledge Editing through Efficient Finetuning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2406.04496)
	1. Tan et al. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions. 2024. [[arxiv]](https://arxiv.org/abs/2406.05688)
	1. Song et al. Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters. 2024. [[arxiv]](https://arxiv.org/abs/2406.05955)
	1. Gu et al. RWKV-CLIP: A Robust Vision-Language Representation Learner. 2024. [[arxiv]](https://arxiv.org/abs/2406.06973)
	1. Chen et al. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. 2024. [[arxiv]](https://arxiv.org/abs/2406.07115)
	1. Zhu et al. Are Large Language Models Good Statisticians?. 2024. [[arxiv]](https://arxiv.org/abs/2406.07815)
	1. Li et al. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2406.10099)
	1. Ding et al. IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce. 2024. [[arxiv]](https://arxiv.org/abs/2406.10173)
	1. He et al. COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities. 2024. [[arxiv]](https://arxiv.org/abs/2406.12074)
	1. Lin et al. FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. 2024. [[arxiv]](https://arxiv.org/abs/2406.14408)
	1. Treutlein et al. Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. 2024. [[arxiv]](https://arxiv.org/abs/2406.14546)
	1. Feng et al. SS-Bench: A Benchmark for Social Story Generation and Evaluation. 2024. [[arxiv]](https://arxiv.org/abs/2406.15695)
	1. Feng et al. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. 2024. [[arxiv]](https://arxiv.org/abs/2406.17233)
	1. Liu et al. Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals. 2024. [[arxiv]](https://arxiv.org/abs/2406.18069)
	1. Iyer et al. Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh’s Submission to AmericasNLP 2024 Translation Task. AmericasNLP 2024. [[paper]](https://aclanthology.org/2024.americasnlp-1.25)
	1. [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

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