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lazyllm-llamafactory

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

https://github.com/hiyouga/llama-factory

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Keywords

agent ai deepseek fine-tuning gemma gpt instruction-tuning large-language-models llama llama3 llm lora moe nlp peft qlora quantization qwen rlhf transformers

Keywords from Contributors

transformer jax cryptography cryptocurrency diffusion language-model langchain gemini anthropic text2image
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Repository

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

Basic Info
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Topics
agent ai deepseek fine-tuning gemma gpt instruction-tuning large-language-models llama llama3 llm lora moe nlp peft qlora quantization qwen rlhf transformers
Created over 2 years ago · Last pushed 4 months ago
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Readme Contributing License Code of conduct Citation Security

README.md

# LLaMA Factory

GitHub Repo stars GitHub last commit GitHub contributors GitHub workflow PyPI Citation Docker Pulls

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Used by Amazon, NVIDIA, Aliyun, etc.

### Supporters |
Warp sponsorship
Warp, the agentic terminal for developers
Available for MacOS, Linux, & Windows | SerpAPI sponsorship | | ---- | ---- | ---- ### Easily fine-tune 100+ large language models with zero-code [CLI](#quickstart) and [Web UI](#fine-tuning-with-llama-board-gui-powered-by-gradio) ![GitHub Trend](https://trendshift.io/api/badge/repositories/4535)

Join our WeChat, NPU, Lab4AI, LLaMA Factory Online user group.

[ English | ]

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

https://github.com/user-attachments/assets/3991a3a8-4276-4d30-9cab-4cb0c4b9b99e

Choose your path:

  • Documentation (WIP): https://llamafactory.readthedocs.io/en/latest/
  • Documentation (AMD GPU): https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/finetune/llamafactory_llama3.html
  • Colab (free): https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
  • Local machine: Please refer to usage
  • PAI-DSW (free trial): https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
  • Alaya NeW (cloud GPU deal): https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory
  • Official Course: https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46?utm_source=LLaMA-Factory
  • LLaMA Factory Online: https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory

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

Table of Contents

Features

  • Various models: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, DeepSeek, Yi, Gemma, 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, BAdam, APOLLO, Adam-mini, Muon, OFT, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA.
  • Practical tricks: FlashAttention-2, Unsloth, Liger Kernel, RoPE scaling, NEFTune and rsLoRA.
  • Wide tasks: Multi-turn dialogue, tool using, image understanding, visual grounding, video recognition, audio understanding, etc.
  • Experiment monitors: LlamaBoard, TensorBoard, Wandb, MLflow, SwanLab, etc.
  • Faster inference: OpenAI-style API, Gradio UI and CLI with vLLM worker or SGLang worker.

Day-N Support for Fine-Tuning Cutting-Edge Models

| Support Date | Model Name | | ------------ | -------------------------------------------------------------------- | | Day 0 | Qwen3 / Qwen2.5-VL / Gemma 3 / GLM-4.1V / InternLM 3 / MiniCPM-o-2.6 | | Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |

Blogs

All Blogs - [Fine-tune Llama3.1-70B for Medical Diagnosis using LLaMA-Factory](https://docs.alayanew.com/docs/documents/bestPractice/bigModel/llama70B/?utm_source=LLaMA-Factory) (Chinese) - [Fine-tune Qwen2.5-VL for Autonomous Driving using LLaMA-Factory](https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory) (Chinese) - [LLaMA Factory: Fine-tuning the DeepSeek-R1-Distill-Qwen-7B Model for News Classifier](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b) (Chinese) - [A One-Stop Code-Free Model Fine-Tuning \& Deployment Platform based on SageMaker and LLaMA-Factory](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/) (Chinese) - [LLaMA Factory Multi-Modal Fine-Tuning Practice: Fine-Tuning Qwen2-VL for Personal Tourist Guide](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl) (Chinese) - [LLaMA Factory: Fine-tuning Llama3 for Role-Playing](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) (Chinese)

Changelog

[25/08/22] We supported OFT and OFTv2. See examples for usage.

[25/08/20] We supported fine-tuning the Intern-S1-mini models. See PR #8976 to get started.

[25/08/06] We supported fine-tuning the GPT-OSS models. See PR #8826 to get started.

Full Changelog [25/07/02] We supported fine-tuning the **[GLM-4.1V-9B-Thinking](https://github.com/THUDM/GLM-4.1V-Thinking)** model. [25/04/28] We supported fine-tuning the **[Qwen3](https://qwenlm.github.io/blog/qwen3/)** model family. [25/04/21] We supported the **[Muon](https://github.com/KellerJordan/Muon)** optimizer. See [examples](examples/README.md) for usage. Thank [@tianshijing](https://github.com/tianshijing)'s PR. [25/04/16] We supported fine-tuning the **[InternVL3](https://huggingface.co/OpenGVLab/InternVL3-8B)** model. See [PR #7258](https://github.com/hiyouga/LLaMA-Factory/pull/7258) to get started. [25/04/14] We supported fine-tuning the **[GLM-Z1](https://huggingface.co/THUDM/GLM-Z1-9B-0414)** and **[Kimi-VL](https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct)** models. [25/04/06] We supported fine-tuning the **[Llama 4](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)** model. See [PR #7611](https://github.com/hiyouga/LLaMA-Factory/pull/7611) to get started. [25/03/31] We supported fine-tuning the **[Qwen2.5 Omni](https://qwenlm.github.io/blog/qwen2.5-omni/)** model. See [PR #7537](https://github.com/hiyouga/LLaMA-Factory/pull/7537) to get started. [25/03/15] We supported **[SGLang](https://github.com/sgl-project/sglang)** as inference backend. Try `infer_backend: sglang` to accelerate inference. [25/03/12] We supported fine-tuning the **[Gemma 3](https://huggingface.co/blog/gemma3)** model. [25/02/24] Announcing **[EasyR1](https://github.com/hiyouga/EasyR1)**, an efficient, scalable and multi-modality RL training framework for efficient GRPO training. [25/02/11] We supported saving the **[Ollama](https://github.com/ollama/ollama)** modelfile when exporting the model checkpoints. See [examples](examples/README.md) for usage. [25/02/05] We supported fine-tuning the **[Qwen2-Audio](Qwen/Qwen2-Audio-7B-Instruct)** and **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** on audio understanding tasks. [25/01/31] We supported fine-tuning the **[DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1)** and **[Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)** models. [25/01/15] We supported **[APOLLO](https://arxiv.org/abs/2412.05270)** optimizer. See [examples](examples/README.md) for usage. [25/01/14] We supported fine-tuning the **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** and **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** models. Thank [@BUAADreamer](https://github.com/BUAADreamer)'s PR. [25/01/14] We supported fine-tuning the **[InternLM 3](https://huggingface.co/collections/internlm/)** models. Thank [@hhaAndroid](https://github.com/hhaAndroid)'s PR. [25/01/10] We supported fine-tuning the **[Phi-4](https://huggingface.co/microsoft/phi-4)** model. [24/12/21] We supported using **[SwanLab](https://github.com/SwanHubX/SwanLab)** for experiment tracking and visualization. See [this section](#use-swanlab-logger) for details. [24/11/27] We supported fine-tuning the **[Skywork-o1](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)** model and the **[OpenO1](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)** dataset. [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 supported fine-tuning the **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** models. [24/08/30] We supported 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 supported **[Liger Kernel](https://github.com/linkedin/Liger-Kernel)**. Try `enable_liger_kernel: true` for efficient training. [24/08/09] We supported **[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. [24/07/04] We supported [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 supported **[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.

[!TIP] If you cannot use the latest feature, please pull the latest code and install LLaMA-Factory again.

Supported Models

| Model | Model size | Template | | ----------------------------------------------------------------- | -------------------------------- | ------------------- | | Baichuan 2 | 7B/13B | baichuan2 | | BLOOM/BLOOMZ | 560M/1.1B/1.7B/3B/7.1B/176B | - | | ChatGLM3 | 6B | chatglm3 | | Command R | 35B/104B | cohere | | DeepSeek (Code/MoE) | 7B/16B/67B/236B | deepseek | | DeepSeek 2.5/3 | 236B/671B | deepseek3 | | DeepSeek R1 (Distill) | 1.5B/7B/8B/14B/32B/70B/671B | deepseekr1 | | Falcon | 7B/11B/40B/180B | falcon | | Falcon-H1 | 0.5B/1.5B/3B/7B/34B | falconh1 | | Gemma/Gemma 2/CodeGemma | 2B/7B/9B/27B | gemma/gemma2 | | Gemma 3/Gemma 3n | 270M/1B/4B/6B/8B/12B/27B | gemma3/gemma3n | | GLM-4/GLM-4-0414/GLM-Z1 | 9B/32B | glm4/glmz1 | | GLM-4.1V | 9B | glm4v | | GLM-4.5/GLM-4.5V* | 106B/355B | glm4moe/glm4vmoe | | GPT-2 | 0.1B/0.4B/0.8B/1.5B | - | | GPT-OSS | 20B/120B | gpt | | Granite 3.0-3.3 | 1B/2B/3B/8B | granite3 | | Granite 4 | 7B | granite4 | | Hunyuan | 7B | hunyuan | | Index | 1.9B | index | | InternLM 2-3 | 7B/8B/20B | intern2 | | InternVL 2.5-3.5 | 1B/2B/4B/8B/14B/30B/38B/78B/241B | internvl | | InternLM/Intern-S1-mini | 8B | interns1 | | Kimi-VL | 16B | kimivl | | Llama | 7B/13B/33B/65B | - | | Llama 2 | 7B/13B/70B | llama2 | | Llama 3-3.3 | 1B/3B/8B/70B | llama3 | | Llama 4 | 109B/402B | llama4 | | Llama 3.2 Vision | 11B/90B | mllama | | LLaVA-1.5 | 7B/13B | llava | | LLaVA-NeXT | 7B/8B/13B/34B/72B/110B | llavanext | | LLaVA-NeXT-Video | 7B/34B | llavanextvideo | | MiMo | 7B | mimo | | MiniCPM | 0.5B/1B/2B/4B/8B | cpm/cpm3/cpm4 | | MiniCPM-o-2.6/MiniCPM-V-2.6 | 8B | minicpmo/minicpmv | | Ministral/Mistral-Nemo | 8B/12B | ministral | | Mistral/Mixtral | 7B/8x7B/8x22B | mistral | | Mistral Small | 24B | mistralsmall | | OLMo | 1B/7B | - | | PaliGemma/PaliGemma2 | 3B/10B/28B | paligemma | | Phi-1.5/Phi-2 | 1.3B/2.7B | - | | Phi-3/Phi-3.5 | 4B/14B | phi | | Phi-3-small | 7B | phismall | | Phi-4 | 14B | phi4 | | Pixtral | 12B | pixtral | | Qwen (1-2.5) (Code/Math/MoE/QwQ) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen | | Qwen3 (MoE/Instruct/Thinking) | 0.6B/1.7B/4B/8B/14B/32B/235B | qwen3/qwen3nothink | | Qwen2-Audio | 7B | qwen2audio | | Qwen2.5-Omni | 3B/7B | qwen2omni | | Qwen2-VL/Qwen2.5-VL/QVQ | 2B/3B/7B/32B/72B | qwen2vl | | Seed Coder | 8B | seedcoder | | Skywork o1 | 8B | skyworko1 | | StarCoder 2 | 3B/7B/15B | - | | TeleChat2 | 3B/7B/35B/115B | telechat2 | | XVERSE | 7B/13B/65B | xverse | | Yi/Yi-1.5 (Code) | 1.5B/6B/9B/34B | yi | | Yi-VL | 6B/34B | yivl | | Yuan 2 | 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.

*: You should install the transformers from main branch and use DISABLE_VERSION_CHECK=1 to skip version check.

**: You need to install a specific version of transformers to use the corresponding model.

Please refer to constants.py for a full list of models we supported.

You also can add a custom chat template to template.py.

Supported Training Approaches

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

[!TIP] The implementation details of PPO can be found in this blog.

Provided Datasets

Pre-training datasets - [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)
Supervised fine-tuning datasets - [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) - [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) - [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub) - [OpenO1-SFT (en&zh)](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT) - [Open-Thoughts (en)](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) - [Open-R1-Math (en)](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) - [Chinese-DeepSeek-R1-Distill (zh)](https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT) - [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)
Preference datasets - [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k) - [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) - [COIG-P (zh)](https://huggingface.co/datasets/m-a-p/COIG-P) - [RLHF-V (en)](https://huggingface.co/datasets/openbmb/RLHF-V-Dataset) - [VLFeedback (en)](https://huggingface.co/datasets/Zhihui/VLFeedback) - [RLAIF-V (en)](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset) - [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)

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 | 3.9 | 3.10 | | torch | 2.0.0 | 2.6.0 | | torchvision | 0.15.0 | 0.21.0 | | transformers | 4.49.0 | 4.50.0 | | datasets | 2.16.0 | 3.2.0 | | accelerate | 0.34.0 | 1.2.1 | | peft | 0.14.0 | 0.15.1 | | trl | 0.8.6 | 0.9.6 |

| Optional | Minimum | Recommend | | ------------ | ------- | --------- | | CUDA | 11.6 | 12.2 | | deepspeed | 0.10.0 | 0.16.4 | | bitsandbytes | 0.39.0 | 0.43.1 | | vllm | 0.4.3 | 0.8.2 | | flash-attn | 2.5.6 | 2.7.2 |

Hardware Requirement

* estimated

| Method | Bits | 7B | 14B | 30B | 70B | xB | | ----------------------------------- | ---- | ----- | ----- | ----- | ------ | ------- | | Full (bf16 or fp16) | 32 | 120GB | 240GB | 600GB | 1200GB | 18xGB | | Full (pure_bf16) | 16 | 60GB | 120GB | 300GB | 600GB | 8xGB | | Freeze/LoRA/GaLore/APOLLO/BAdam/OFT | 16 | 16GB | 32GB | 64GB | 160GB | 2xGB | | QLoRA / QOFT | 8 | 10GB | 20GB | 40GB | 80GB | xGB | | QLoRA / QOFT | 4 | 6GB | 12GB | 24GB | 48GB | x/2GB | | QLoRA / QOFT | 2 | 4GB | 8GB | 16GB | 24GB | x/4GB |

Getting Started

Installation

[!IMPORTANT] Installation is mandatory.

Install from Source

bash git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git cd LLaMA-Factory pip install -e ".[torch,metrics]" --no-build-isolation

Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, aqlm, vllm, sglang, galore, apollo, badam, adam-mini, qwen, minicpm_v, openmind, swanlab, dev

Install from Docker Image

bash docker run -it --rm --gpus=all --ipc=host hiyouga/llamafactory:latest

This image is built on Ubuntu 22.04 (x86_64), CUDA 12.4, Python 3.11, PyTorch 2.6.0, and Flash-attn 2.7.4.

Find the pre-built images: https://hub.docker.com/r/hiyouga/llamafactory/tags

Please refer to build docker to build the image yourself.

Setting up a virtual environment with uv Create an isolated Python environment with [uv](https://github.com/astral-sh/uv): ```bash uv sync --extra torch --extra metrics --prerelease=allow ``` Run LLaMA-Factory in the isolated environment: ```bash uv run --prerelease=allow llamafactory-cli train examples/train_lora/llama3_lora_pretrain.yaml ```
For Windows users #### Install PyTorch You need to manually install the GPU version of PyTorch on the Windows platform. Please refer to the [official website](https://pytorch.org/get-started/locally/) and the following command to install PyTorch with CUDA support: ```bash pip uninstall torch torchvision torchaudio pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 python -c "import torch; print(torch.cuda.is_available())" ``` If you see `True` then you have successfully installed PyTorch with CUDA support. Try `dataloader_num_workers: 0` if you encounter `Can't pickle local object` error. #### Install BitsAndBytes 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 ``` #### Install Flash Attention-2 To enable FlashAttention-2 on the Windows platform, please use the script from [flash-attention-windows-wheel](https://huggingface.co/lldacing/flash-attention-windows-wheel) to compile and install it by yourself.
For Ascend NPU users To install LLaMA Factory on Ascend NPU devices, please upgrade Python to version 3.10 or higher and 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%20V100R001C20SPC702/Ascend-cann-toolkit_8.0.0.alpha002_linux-"$(uname -i)".run bash Ascend-cann-toolkit_8.0.0.alpha002_linux-"$(uname -i)".run --install # install CANN Kernels wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C20SPC702/Ascend-cann-kernels-910b_8.0.0.alpha002_linux-"$(uname -i)".run bash Ascend-cann-kernels-910b_8.0.0.alpha002_linux-"$(uname -i)".run --install # set env variables source /usr/local/Ascend/ascend-toolkit/set_env.sh ``` | Requirement | Minimum | Recommend | | ------------ | ------- | -------------- | | CANN | 8.0.RC1 | 8.0.0.alpha002 | | torch | 2.1.0 | 2.4.0 | | torch-npu | 2.1.0 | 2.4.0.post2 | | deepspeed | 0.13.2 | 0.13.2 | | vllm-ascend | - | 0.7.3 | 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) #### Install BitsAndBytes To use QLoRA based on bitsandbytes on Ascend NPU, please follow these 3 steps: 1. Manually compile bitsandbytes: Refer to [the installation documentation](https://huggingface.co/docs/bitsandbytes/installation?backend=Ascend+NPU&platform=Ascend+NPU) for the NPU version of bitsandbytes to complete the compilation and installation. The compilation requires a cmake version of at least 3.22.1 and a g++ version of at least 12.x. ```bash # Install bitsandbytes from source # Clone bitsandbytes repo, Ascend NPU backend is currently enabled on multi-backend-refactor branch git clone -b multi-backend-refactor https://github.com/bitsandbytes-foundation/bitsandbytes.git cd bitsandbytes/ # Install dependencies pip install -r requirements-dev.txt # Install the dependencies for the compilation tools. Note that the commands for this step may vary depending on the operating system. The following are provided for reference apt-get install -y build-essential cmake # Compile & install cmake -DCOMPUTE_BACKEND=npu -S . make pip install . ``` 2. Install transformers from the main branch. ```bash git clone -b main https://github.com/huggingface/transformers.git cd transformers pip install . ``` 3. Set `double_quantization: false` in the configuration. You can refer to the [example](examples/train_qlora/llama3_lora_sft_bnb_npu.yaml).

Data Preparation

Please refer to data/README.md for checking the details about the format of dataset files. You can use datasets on HuggingFace / ModelScope / Modelers hub, load the dataset in local disk, or specify a path to s3/gcs cloud storage.

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

You can also use Easy Dataset, DataFlow and GraphGen to create synthetic data for fine-tuning.

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 for advanced usage (including distributed training).

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

Read FAQs first if you encounter any problems.

Fine-Tuning with LLaMA Board GUI (powered by Gradio)

bash llamafactory-cli webui

LLaMA Factory Online

Read our documentation.

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

Build without Docker Compose For CUDA users: ```bash docker build -f ./docker/docker-cuda/Dockerfile \ --build-arg PIP_INDEX=https://pypi.org/simple \ --build-arg EXTRAS=metrics \ -t llamafactory:latest . docker run -dit --ipc=host --gpus=all \ -p 7860:7860 \ -p 8000:8000 \ --name llamafactory \ llamafactory:latest docker exec -it llamafactory bash ``` For Ascend NPU users: ```bash docker build -f ./docker/docker-npu/Dockerfile \ --build-arg PIP_INDEX=https://pypi.org/simple \ --build-arg EXTRAS=torch-npu,metrics \ -t llamafactory:latest . docker run -dit --ipc=host \ -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 \ --name llamafactory \ llamafactory:latest docker exec -it llamafactory bash ``` For AMD ROCm users: ```bash docker build -f ./docker/docker-rocm/Dockerfile \ --build-arg PIP_INDEX=https://pypi.org/simple \ --build-arg EXTRAS=metrics \ -t llamafactory:latest . docker run -dit --ipc=host \ -p 7860:7860 \ -p 8000:8000 \ --device /dev/kfd \ --device /dev/dri \ --name llamafactory \ llamafactory:latest docker exec -it llamafactory bash ```
Use Docker volumes You can uncomment `VOLUME [ "/root/.cache/huggingface", "/app/shared_data", "/app/output" ]` in the Dockerfile to use data volumes. When building the Docker image, use `-v ./hf_cache:/root/.cache/huggingface` argument to mount the local directory to the container. The following data volumes are available. - `hf_cache`: Utilize Hugging Face cache on the host machine. - `shared_data`: The directionary to store datasets on the host machine. - `output`: Set export dir to this location so that the merged result can be accessed directly on the host machine.

Deploy with OpenAI-style API and vLLM

bash API_PORT=8000 llamafactory-cli api examples/inference/llama3.yaml infer_backend=vllm vllm_enforce_eager=true

[!TIP] Visit this page for API document.

Examples: Image understanding | Function calling

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, 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, e.g., TeleAI/TeleChat-7B-pt.

Use W&B Logger

To use Weights & Biases 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 when launching training tasks to log in with your W&B account.

Use SwanLab Logger

To use SwanLab for logging experimental results, you need to add the following arguments to yaml files.

yaml use_swanlab: true swanlab_run_name: test_run # optional

When launching training tasks, you can log in to SwanLab in three ways:

  1. Add swanlab_api_key=<your_api_key> to the yaml file, and set it to your API key.
  2. Set the environment variable SWANLAB_API_KEY to your API key.
  3. Use the swanlab login command to complete the login.

Projects using LLaMA Factory

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

Click to show 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) 1. Wang et al. Cybernetic Sentinels: Unveiling the Impact of Safety Data Selection on Model Security in Supervised Fine-Tuning. ICIC 2024. [[paper]](https://link.springer.com/chapter/10.1007/978-981-97-5669-8_23) 1. Xia et al. Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2408.04693) 1. Zeng et al. Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2408.04168) 1. Xia et al. Using Pre-trained Language Model for Accurate ESG Prediction. FinNLP 2024. [[paper]](https://aclanthology.org/2024.finnlp-2.1/) 1. Liang et al. I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm. 2024. [[arxiv]](https://arxiv.org/abs/2408.08072) 1. Bai et al. Aligning Large Language Model with Direct Multi-Preference Optimization for Recommendation. CIKM 2024. [[paper]](https://dl.acm.org/doi/10.1145/3627673.3679611) 1. Zhang et al. CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling. ACL 2024. [[paper]](https://aclanthology.org/2024.findings-acl.830.pdf) 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. 1. **[AutoRE](https://github.com/THUDM/AutoRE)**: A document-level relation extraction system based on large language models. 1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**: SDKs for fine-tuning LLMs on Windows PC for NVIDIA RTX. 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. 1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**: A full pipeline for RAG retrieval model fine-tuning, inference, and distillation. [[blog]](https://zhuanlan.zhihu.com/p/987727357) 1. **[360-LLaMA-Factory](https://github.com/Qihoo360/360-LLaMA-Factory)**: A modified library that supports long sequence SFT & DPO using ring attention. 1. **[Sky-T1](https://novasky-ai.github.io/posts/sky-t1/)**: An o1-like model fine-tuned by NovaSky AI with very small cost. 1. **[WeClone](https://github.com/xming521/WeClone)**: One-stop solution for creating your digital avatar from chat logs. 1. **[EmoLLM](https://github.com/SmartFlowAI/EmoLLM)**: A project about large language models (LLMs) and mental health.

License

This repository is licensed under the Apache-2.0 License.

Please follow the model licenses to use the corresponding model weights: Baichuan 2 / BLOOM / ChatGLM3 / Command R / DeepSeek / Falcon / Gemma / GLM-4 / GPT-2 / Granite / Index / InternLM / Llama / Llama 2 / Llama 3 / Llama 4 / MiniCPM / Mistral/Mixtral/Pixtral / OLMo / Phi-1.5/Phi-2 / Phi-3/Phi-4 / Qwen / Skywork / StarCoder 2 / TeleChat2 / XVERSE / Yi / Yi-1.5 / Yuan 2

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, TRL, QLoRA and FastChat. Thanks for their wonderful works.

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Owner

  • Name: hoshi-hiyouga
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  • Company: 死んだ魚の目日照不足シャトルラン部

No code All live

Citation (CITATION.cff) 

cff-version: 1.2.0
date-released: 2024-03
message: "If you use this software, please cite it as below."
authors:
- family-names: "Zheng"
  given-names: "Yaowei"
- family-names: "Zhang"
  given-names: "Richong"
- family-names: "Zhang"
  given-names: "Junhao"
- family-names: "Ye"
  given-names: "Yanhan"
- family-names: "Luo"
  given-names: "Zheyan"
- family-names: "Feng"
  given-names: "Zhangchi"
- family-names: "Ma"
  given-names: "Yongqiang"
title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
url: "https://arxiv.org/abs/2403.13372"
preferred-citation:
  type: conference-paper
  conference:
    name: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)"
  authors:
    - family-names: "Zheng"
      given-names: "Yaowei"
    - family-names: "Zhang"
      given-names: "Richong"
    - family-names: "Zhang"
      given-names: "Junhao"
    - family-names: "Ye"
      given-names: "Yanhan"
    - family-names: "Luo"
      given-names: "Zheyan"
    - family-names: "Feng"
      given-names: "Zhangchi"
    - family-names: "Ma"
      given-names: "Yongqiang"
  title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
  url: "https://arxiv.org/abs/2403.13372"
  year: 2024
  publisher: "Association for Computational Linguistics"
  address: "Bangkok, Thailand"

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Unified Efficient Fine-Tuning of 100+ LLMs

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Dependencies

pyproject.toml pypi
requirements.txt pypi
  • accelerate >=0.21.0
  • datasets >=2.12.0
  • fastapi ==0.95.1
  • gradio >=3.36.0
  • jieba *
  • matplotlib *
  • nltk *
  • peft >=0.4.0
  • protobuf *
  • pydantic ==1.10.11
  • rouge-chinese *
  • scipy *
  • sentencepiece *
  • sse-starlette *
  • tiktoken *
  • torch >=1.13.1
  • transformers >=4.30.0
  • trl >=0.7.1
  • uvicorn *
setup.py pypi