Développement
peft
Fine-tune huge models on small hardware with LoRA and other efficient methods
★ 21,8 k étoilesApache-2.0
Voir le guide →LlamaFactory est un projet de développement open source, créé par hiyouga (hiyouga/LlamaFactory sur GitHub). Il compte 75 362 étoiles sur GitHub et 9 239 forks, il est publié sous la licence Apache-2.0 et a été mis à jour pour la dernière fois sur GitHub le 28 septembre 2026.
Unified, efficient fine-tuning of 100+ LLMs and VLMs with a CLI and Web UI — ACL 2024
Decide what you want from LlamaFactory and check the README and requirements to confirm it fits your setup and Python environment.
Clone the repository from GitHub (github.com/hiyouga/LlamaFactory) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Fine-tune over 100 LLMs and vision-language models including Llama, Qwen, Gemma and DeepSeek families.
Supports pre-training, SFT, RLHF, LoRA, QLoRA and quantization workflows.
Llama Board GUI (Gradio) lets you configure and launch training without writing code.
Actively adds new cutting-edge models as they are released.
Ships curated datasets for instruction tuning and preference training.
Official Docker images, including Ascend NPU variants, for reproducible training.
Start by getting the source and entering the project directory.
git clone https://github.com/hiyouga/LlamaFactory.git
cd LlamaFactoryClone shallow and install in editable mode, per the README quickstart.
git clone --depth 1 https://github.com/hiyouga/LlamaFactory.git
cd LlamaFactory
pip install -e .Start Llama Board to configure your first fine-tune in the browser.
llamafactory-cli webui # or: uv run llamafactory-cli webuiIt is maintained by hiyouga and contributors on GitHub, where the repository github.com/hiyouga/LlamaFactory is the canonical home for code, issues and releases.
Yes — it is open source under the Apache-2.0 license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
No API key needed — training is local on your hardware; you need a suitable GPU for most models.
Compared with Hugging Face TRL alone: LlamaFactory unifies many models, methods and datasets behind one CLI and Web UI instead of wiring libraries together yourself.
Clone github.com/hiyouga/LlamaFactory, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
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