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

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	👋 Join our [WeChat](assets/wechat.jpg) or [NPU user group](assets/wechat_npu.jpg).

	\[ English \| [中文](README_zh.md) \]

	Fine-tuning a large language model can be easy as...

	https://github.com/user-attachments/assets/7c96b465-9df7-45f4-8053-bf03e58386d3

	Choose your path:

	- Colab: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
	- PAI-DSW: [Llama3 Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) \| [Qwen2-VL Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)
	- Local machine: Please refer to [usage](#getting-started)
	- Documentation (WIP): https://llamafactory.readthedocs.io/zh-cn/latest/

	> [!NOTE]
	> Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.

	## Table of Contents

	- [Features](#features)
	- [Benchmark](#benchmark)
	- [Changelog](#changelog)
	- [Supported Models](#supported-models)
	- [Supported Training Approaches](#supported-training-approaches)
	- [Provided Datasets](#provided-datasets)
	- [Requirement](#requirement)
	- [Getting Started](#getting-started)
	- [Projects using LLaMA Factory](#projects-using-llama-factory)
	- [License](#license)
	- [Citation](#citation)
	- [Acknowledgement](#acknowledgement)

	## Features

	- Various models: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
	- Integrated methods: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
	- Scalable resources: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
	- Advanced algorithms: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
	- Practical tricks: [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 and rsLoRA.
	- Experiment monitors: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
	- Faster inference: OpenAI-style API, Gradio UI and CLI with vLLM worker.

	## Benchmark

	Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning), LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.

	![benchmark](assets/benchmark.svg)

	<details><summary>Definitions</summary>

	- Training Speed: the number of training samples processed per second during the training. (bs=4, cutoff_len=1024)
	- Rouge Score: Rouge-2 score on the development set of the [advertising text generation](https://aclanthology.org/D19-1321.pdf) task. (bs=4, cutoff_len=1024)
	- GPU Memory: Peak GPU memory usage in 4-bit quantized training. (bs=1, cutoff_len=1024)
	- We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA Factory's LoRA tuning.

	</details>

	## Changelog

	[24/10/09] We supported downloading pre-trained models and datasets from the [Modelers Hub](https://modelers.cn/models). See [this tutorial](#download-from-modelers-hub) for usage.

	[24/09/19] We support fine-tuning the [Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/) models.

	[24/08/30] We support fine-tuning the [Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/) models. Thank [@simonJJJ](https://github.com/simonJJJ)'s PR.

	[24/08/27] We support [Liger Kernel](https://github.com/linkedin/Liger-Kernel). Try `enable_liger_kernel: true` for efficient training.

	[24/08/09] We support [Adam-mini](https://github.com/zyushun/Adam-mini) optimizer. See [examples](examples/README.md) for usage. Thank [@relic-yuexi](https://github.com/relic-yuexi)'s PR.

	<details><summary>Full Changelog</summary>

	[24/07/04] We support [contamination-free packed training](https://github.com/MeetKai/functionary/tree/main/functionary/train/packing). Use `neat_packing: true` to activate it. Thank [@chuan298](https://github.com/chuan298)'s PR.

	[24/06/16] We support [PiSSA](https://arxiv.org/abs/2404.02948) algorithm. See [examples](examples/README.md) for usage.

	[24/06/07] We supported fine-tuning the [Qwen2](https://qwenlm.github.io/blog/qwen2/) and [GLM-4](https://github.com/THUDM/GLM-4) models.

	[24/05/26] We supported [SimPO](https://arxiv.org/abs/2405.14734) algorithm for preference learning. See [examples](examples/README.md) for usage.

	[24/05/20] We supported fine-tuning the PaliGemma series models. Note that the PaliGemma models are pre-trained models, you need to fine-tune them with `paligemma` template for chat completion.

	[24/05/18] We supported [KTO](https://arxiv.org/abs/2402.01306) algorithm for preference learning. See [examples](examples/README.md) for usage.

	[24/05/14] We supported training and inference on the Ascend NPU devices. Check [installation](#installation) section for details.

	[24/04/26] We supported fine-tuning the LLaVA-1.5 multimodal LLMs. See [examples](examples/README.md) for usage.

	[24/04/22] We provided a [Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing) for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details.

	[24/04/21] We supported [Mixture-of-Depths](https://arxiv.org/abs/2404.02258) according to [AstraMindAI's implementation](https://github.com/astramind-ai/Mixture-of-depths). See [examples](examples/README.md) for usage.

	[24/04/16] We supported [BAdam](https://arxiv.org/abs/2404.02827) optimizer. See [examples](examples/README.md) for usage.

	[24/04/16] We supported [unsloth](https://github.com/unslothai/unsloth)'s long-sequence training (Llama-2-7B-56k within 24GB). It achieves 117% speed and 50% memory compared with FlashAttention-2, more benchmarks can be found in [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison).

	[24/03/31] We supported [ORPO](https://arxiv.org/abs/2403.07691). See [examples](examples/README.md) for usage.

	[24/03/21] Our paper "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" is available at arXiv!

	[24/03/20] We supported FSDP+QLoRA that fine-tunes a 70B model on 2x24GB GPUs. See [examples](examples/README.md) for usage.

	[24/03/13] We supported [LoRA+](https://arxiv.org/abs/2402.12354). See [examples](examples/README.md) for usage.

	[24/03/07] We supported [GaLore](https://arxiv.org/abs/2403.03507) optimizer. See [examples](examples/README.md) for usage.

	[24/03/07] We integrated [vLLM](https://github.com/vllm-project/vllm) for faster and concurrent inference. Try `infer_backend: vllm` to enjoy 270% inference speed.

	[24/02/28] We supported weight-decomposed LoRA ([DoRA](https://arxiv.org/abs/2402.09353)). Try `use_dora: true` to activate DoRA training.

	[24/02/15] We supported block expansion proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See [examples](examples/README.md) for usage.

	[24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this [blog post](https://qwenlm.github.io/blog/qwen1.5/) for details.

	[24/01/18] We supported agent tuning for most models, equipping model with tool using abilities by fine-tuning with `dataset: glaive_toolcall_en`.

	[23/12/23] We supported [unsloth](https://github.com/unslothai/unsloth)'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `use_unsloth: true` argument to activate unsloth patch. It achieves 170% speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details.

	[23/12/12] We supported fine-tuning the latest MoE model [Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) in our framework. See hardware requirement [here](#hardware-requirement).

	[23/12/01] We supported downloading pre-trained models and datasets from the [ModelScope Hub](https://modelscope.cn/models). See [this tutorial](#download-from-modelscope-hub) for usage.

	[23/10/21] We supported [NEFTune](https://arxiv.org/abs/2310.05914) trick for fine-tuning. Try `neftune_noise_alpha: 5` argument to activate NEFTune.

	[23/09/27] We supported $S^2$-Attn proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `shift_attn: true` argument to enable shift short attention.

	[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [examples](examples/README.md) for usage.

	[23/09/10] We supported [FlashAttention-2](https://github.com/Dao-AILab/flash-attention). Try `flash_attn: fa2` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.

	[23/08/12] We supported RoPE scaling to extend the context length of the LLaMA models. Try `rope_scaling: linear` argument in training and `rope_scaling: dynamic` argument at inference to extrapolate the position embeddings.

	[23/08/11] We supported [DPO training](https://arxiv.org/abs/2305.18290) for instruction-tuned models. See [examples](examples/README.md) for usage.

	[23/07/31] We supported dataset streaming. Try `streaming: true` and `max_steps: 10000` arguments to load your dataset in streaming mode.

	[23/07/29] We released two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)) for details.

	[23/07/18] We developed an all-in-one Web UI for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development.

	[23/07/09] We released [FastEdit](https://github.com/hiyouga/FastEdit) ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested.

	[23/06/29] We provided a reproducible example of training a chat model using instruction-following datasets, see [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft) for details.

	[23/06/22] We aligned the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in arbitrary ChatGPT-based applications.

	[23/06/03] We supported quantized training and inference (aka [QLoRA](https://github.com/artidoro/qlora)). See [examples](examples/README.md) for usage.

	</details>

	## Supported Models

	\| Model \| Model size \| 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/InternLM2.5](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 3-3.2](https://huggingface.co/meta-llama) \| 1B/3B/8B/70B \| llama3 \|
	\| [LLaVA-1.5](https://huggingface.co/llava-hf) \| 7B/13B \| llava \|
	\| [LLaVA-NeXT](https://huggingface.co/llava-hf) \| 7B/8B/13B/34B/72B/110B \| llava_next \|
	\| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf) \| 7B/34B \| llava_next_video \|
	\| [MiniCPM](https://huggingface.co/openbmb) \| 1B/2B/4B \| cpm/cpm3 \|
	\| [Mistral/Mixtral](https://huggingface.co/mistralai) \| 7B/8x7B/8x22B \| mistral \|
	\| [OLMo](https://huggingface.co/allenai) \| 1B/7B \| - \|
	\| [PaliGemma](https://huggingface.co/google) \| 3B \| paligemma \|
	\| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) \| 1.3B/2.7B \| - \|
	\| [Phi-3](https://huggingface.co/microsoft) \| 4B/7B/14B \| phi \|
	\| [Qwen (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen) \| 0.5B/1.5B/3B/7B/14B/32B/72B/110B \| qwen \|
	\| [Qwen2-VL](https://huggingface.co/Qwen) \| 2B/7B/72B \| qwen2_vl \|
	\| [StarCoder 2](https://huggingface.co/bigcode) \| 3B/7B/15B \| - \|
	\| [XVERSE](https://huggingface.co/xverse) \| 7B/13B/65B \| xverse \|
	\| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) \| 1.5B/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]
	> For the "base" models, the `template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the corresponding template for the "instruct/chat" models.
	>
	> Remember to use the SAME template in training and inference.

	Please refer to [constants.py](src/llamafactory/extras/constants.py) for a full list of models we supported.

	You also can add a custom chat template to [template.py](src/llamafactory/data/template.py).

	## Supported Training Approaches

	\| Approach \| Full-tuning \| Freeze-tuning \| LoRA \| QLoRA \|
	\| ---------------------- \| ------------------ \| ------------------ \| ------------------ \| ------------------ \|
	\| Pre-Training \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| Supervised Fine-Tuning \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| Reward Modeling \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| PPO Training \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| DPO Training \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| KTO Training \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| ORPO Training \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|
	\| SimPO Training \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \| :white_check_mark: \|

	> [!TIP]
	> The implementation details of PPO can be found in [this blog](https://newfacade.github.io/notes-on-reinforcement-learning/17-ppo-trl.html).

	## Provided Datasets

	<details><summary>Pre-training datasets</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>Supervised fine-tuning datasets</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)
	- [Magpie-ultra-v0.1 (en)](https://huggingface.co/datasets/argilla/magpie-ultra-v0.1)
	- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
	- [Pokemon-gpt4o-captions (en&zh)](https://huggingface.co/datasets/jugg1024/pokemon-gpt4o-captions)
	- [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>Preference datasets</summary>

	- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
	- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
	- [RLHF-V (en)](https://huggingface.co/datasets/openbmb/RLHF-V-Dataset)
	- [VLFeedback (en)](https://huggingface.co/datasets/Zhihui/VLFeedback)
	- [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>

	Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.

	```bash
	pip install --upgrade huggingface_hub
	huggingface-cli login
	```

	## Requirement

	\| Mandatory \| Minimum \| Recommend \|
	\| ------------ \| ------- \| --------- \|
	\| python \| 3.8 \| 3.11 \|
	\| torch \| 1.13.1 \| 2.4.0 \|
	\| transformers \| 4.41.2 \| 4.43.4 \|
	\| datasets \| 2.16.0 \| 2.20.0 \|
	\| accelerate \| 0.30.1 \| 0.32.0 \|
	\| peft \| 0.11.1 \| 0.12.0 \|
	\| trl \| 0.8.6 \| 0.9.6 \|

	\| Optional \| Minimum \| Recommend \|
	\| ------------ \| ------- \| --------- \|
	\| CUDA \| 11.6 \| 12.2 \|
	\| deepspeed \| 0.10.0 \| 0.14.0 \|
	\| bitsandbytes \| 0.39.0 \| 0.43.1 \|
	\| vllm \| 0.4.3 \| 0.5.0 \|
	\| flash-attn \| 2.3.0 \| 2.6.3 \|

	### Hardware Requirement

	\* estimated

	\| Method \| Bits \| 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 \|

	## Getting Started

	### Installation

	> [!IMPORTANT]
	> Installation is mandatory.

	```bash
	git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
	cd LLaMA-Factory
	pip install -e ".[torch,metrics]"
	```

	Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, openmind, quality

	> [!TIP]
	> Use `pip install --no-deps -e .` to resolve package conflicts.

	<details><summary>For Windows users</summary>

	If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you need to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.2, please select the appropriate [release version](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels) based on your CUDA version.

	```bash
	pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
	```

	To enable FlashAttention-2 on the Windows platform, you need to install the precompiled `flash-attn` library, which supports CUDA 12.1 to 12.2. Please download the corresponding version from [flash-attention](https://github.com/bdashore3/flash-attention/releases) based on your requirements.

	</details>

	<details><summary>For Ascend NPU users</summary>

	To install LLaMA Factory on Ascend NPU devices, please specify extra dependencies: `pip install -e ".[torch-npu,metrics]"`. Additionally, you need to install the [Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann). Please follow the [installation tutorial](https://www.hiascend.com/document/detail/en/CANNCommunityEdition/600alphaX/softwareinstall/instg/atlasdeploy_03_0031.html) or use the following commands:

	```bash
	# replace the url according to your CANN version and devices
	# install 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

	# install 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

	# set env variables
	source /usr/local/Ascend/ascend-toolkit/set_env.sh
	```

	\| Requirement \| Minimum \| Recommend \|
	\| ------------ \| ------- \| ----------- \|
	\| 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 \|

	Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.

	If you cannot infer model on NPU devices, try setting `do_sample: false` in the configurations.

	Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) \| [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)

	</details>

	### Data Preparation

	Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope / Modelers hub or load the dataset in local disk.

	> [!NOTE]
	> Please update `data/dataset_info.json` to use your custom dataset.

	### Quickstart

	Use the following 3 commands to run LoRA fine-tuning, inference and merging of the Llama3-8B-Instruct model, respectively.

	```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
	```

	See [examples/README.md](examples/README.md) for advanced usage (including distributed training).

	> [!TIP]
	> Use `llamafactory-cli help` to show help information.

	### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))

	```bash
	llamafactory-cli webui
	```

	### Build Docker

	For CUDA users:

	```bash
	cd docker/docker-cuda/
	docker compose up -d
	docker compose exec llamafactory bash
	```

	For Ascend NPU users:

	```bash
	cd docker/docker-npu/
	docker compose up -d
	docker compose exec llamafactory bash
	```

	For AMD ROCm users:

	```bash
	cd docker/docker-rocm/
	docker compose up -d
	docker compose exec llamafactory bash
	```

	<details><summary>Build without Docker Compose</summary>

	For CUDA users:

	```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 ./om_cache:/root/.cache/openmind \
	-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
	```

	For Ascend NPU users:

	```bash
	# Choose docker image upon your environment
	docker build -f ./docker/docker-npu/Dockerfile \
	--build-arg INSTALL_DEEPSPEED=false \
	--build-arg PIP_INDEX=https://pypi.org/simple \
	-t llamafactory:latest .

	# Change `device` upon your resources
	docker run -dit \
	-v ./hf_cache:/root/.cache/huggingface \
	-v ./ms_cache:/root/.cache/modelscope \
	-v ./om_cache:/root/.cache/openmind \
	-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
	```

	For AMD ROCm users:

	```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 ./om_cache:/root/.cache/openmind \
	-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>Details about volume</summary>

	- `hf_cache`: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
	- `ms_cache`: Similar to Hugging Face cache but for ModelScope users.
	- `om_cache`: Similar to Hugging Face cache but for Modelers users.
	- `data`: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
	- `output`: Set export dir to this location so that the merged result can be accessed directly on the host machine.

	</details>

	### Deploy with OpenAI-style API and vLLM

	```bash
	API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
	```

	> [!TIP]
	> Visit [this page](https://platform.openai.com/docs/api-reference/chat/create) for API document.

	### Download from ModelScope Hub

	If you have trouble with downloading models and datasets from Hugging Face, you can use ModelScope.

	```bash
	export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
	```

	Train the model by specifying a model ID of the ModelScope Hub as the `model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `LLM-Research/Meta-Llama-3-8B-Instruct`.

	### Download from Modelers Hub

	You can also use Modelers Hub to download models and datasets.

	```bash
	export USE_OPENMIND_HUB=1 # `set USE_OPENMIND_HUB=1` for Windows
	```

	Train the model by specifying a model ID of the Modelers Hub as the `model_name_or_path`. You can find a full list of model IDs at [Modelers Hub](https://modelers.cn/models), e.g., `TeleAI/TeleChat-7B-pt`.

	### Use W&B Logger

	To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments to yaml files.

	```yaml
	report_to: wandb
	run_name: test_run # optional
	```

	Set `WANDB_API_KEY` to [your key](https://wandb.ai/authorize) when launching training tasks to log in with your W&B account.

	## Projects using LLaMA Factory

	If you have a project that should be incorporated, please contact via email or create a pull request.

	<details><summary>Click to show</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. Li et al. Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring. 2024. [[arxiv]](https://arxiv.org/abs/2406.19949)
	1. Yang et al. Financial Knowledge Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2407.00365)
	1. Lin et al. DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging. 2024. [[arxiv]](https://arxiv.org/abs/2407.01470)
	1. Bako et al. Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualization. 2024. [[arxiv]](https://arxiv.org/abs/2407.06129)
	1. Huang et al. RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization. 2024. [[arxiv]](https://arxiv.org/abs/2407.08044)
	1. Jiang et al. LLM-Collaboration on Automatic Science Journalism for the General Audience. 2024. [[arxiv]](https://arxiv.org/abs/2407.09756)
	1. Inouye et al. Applied Auto-tuning on LoRA Hyperparameters. 2024. [[paper]](https://scholarcommons.scu.edu/cseng_senior/272/)
	1. Qi et al. Research on Tibetan Tourism Viewpoints information generation system based on LLM. 2024. [[arxiv]](https://arxiv.org/abs/2407.13561)
	1. Xu et al. Course-Correction: Safety Alignment Using Synthetic Preferences. 2024. [[arxiv]](https://arxiv.org/abs/2407.16637)
	1. Sun et al. LAMBDA: A Large Model Based Data Agent. 2024. [[arxiv]](https://arxiv.org/abs/2407.17535)
	1. Zhu et al. CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2407.19705)
	1. Yu et al. Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2408.00137)
	1. Xie et al. The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning. IALP 2024. [[paper]](https://www.asianlp.sg/conferences/ialp2024/proceedings/papers/IALP2024_P055.pdf)
	1. Liu et al. Instruct-Code-Llama: Improving Capabilities of Language Model in Competition Level Code Generation by Online Judge Feedback. ICIC 2024. [[paper]](https://link.springer.com/chapter/10.1007/978-981-97-5669-8_11)
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	1. Xia et al. Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2408.04693)
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	1. [StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper): A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
	1. [DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM): A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
	1. [Sunsimiao](https://github.com/X-D-Lab/Sunsimiao): A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
	1. [CareGPT](https://github.com/WangRongsheng/CareGPT): A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
	1. [MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/): A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods.
	1. [Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3): A large language model specialized in generate metadata for stable diffusion. [[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
	1. [Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med): A multimodal large language model specialized in Chinese medical domain, based on LLaVA-1.5-7B.
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	1. [LazyLLM](https://github.com/LazyAGI/LazyLLM): An easy and lazy way for building multi-agent LLMs applications and supports model fine-tuning via LLaMA Factory.

	</details>

	## License

	This repository is licensed under the [Apache-2.0 License](LICENSE).

	Please follow the model licenses to use the corresponding model weights: [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/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [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)

	## Citation

	If this work is helpful, please kindly cite as:

	```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}
	}
	```

	## Acknowledgement

	This repo benefits from [PEFT](https://github.com/huggingface/peft), [TRL](https://github.com/huggingface/trl), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works.

	## Star History

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