LLaMA-Factory is an open-source unified framework for efficient fine-tuning of more than 100 large language models and vision-language models. It was created by Yaowei Zheng (GitHub: hiyouga) and colleagues at Beihang University's School of Computer Science and Engineering, and was presented as an ACL 2024 System Demonstrations paper, "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models".
The framework enables zero-code fine-tuning through the Gradio-based LLaMA Board web UI, alongside a command-line interface. Supported training methods include continuous pre-training, supervised fine-tuning, reward modeling, and preference and RL methods such as PPO, DPO, KTO, ORPO, and SimPO. Parameter-efficient options cover LoRA, QLoRA, DoRA, and OFT, plus full 16-bit fine-tuning and memory-efficient optimizers including GaLore, BAdam, APOLLO, Adam-mini, and PiSSA.
Supported model families include Llama, Qwen3, DeepSeek, Gemma, GLM-4, Mistral, Phi, and Yi, as well as multimodal models such as LLaVA and Qwen-VL. Fine-tuned models can be served through an OpenAI-compatible API with vLLM integration.
LLaMA-Factory is free under the Apache 2.0 license with no paid tier. It is one of the most popular fine-tuning projects on GitHub, with roughly 73,000 stars, and remains under very active development with documentation at llamafactory.readthedocs.io.