LlamaFactory

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

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

Start local training:

• Please refer to usage

Start cloud training:

• Colab (free): https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
• PAI-DSW (free trial): https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory

Read technical notes:

• Documentation (WIP): https://llamafactory.readthedocs.io/en/latest/
• Documentation (AMD GPU): https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/fine_tune/llama_factory_llama3.html
• Documentation (ASCEND NPU): https://llamafactory.readthedocs.io/en/latest/multibackend/npu/index.html
• Official Blog: https://blog.llamafactory.net/en/

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

Table of Contents

• Features
• Blogs
• Changelog
• Supported Models
• Supported Training Approaches
• Provided Datasets
• Requirement
• Getting Started
  • Installation
  • Data Preparation
  • Quickstart
  • Fine-Tuning with LLaMA Board GUI
  • Build Docker
  • Deploy with OpenAI-style API and vLLM
  • Download from ModelScope Hub
  • Download from Modelers Hub
  • Use W&B Logger
  • Use SwanLab Logger
• Projects using LLaMA Factory
• License
• Citation
• Acknowledgement

Features

• Various models: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen3, Qwen3-VL, DeepSeek, Gemma, GLM, 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, LLaM