LLaMA Factory is a unified framework for fine-tuning large language models that supports over 100 pre-trained models and multiple training methods. It provides a no-code interface for local fine-tuning, making it accessible to both researchers and production engineers who need to adapt foundation models to specific tasks.
Supported Models and Methods
- Over 100 pre-trained models including LLaMA, Qwen, Mistral, Gemma, and ChatGLM out of the box
- Full training pipeline spanning pre-training, supervised fine-tuning, reward modeling, and preference alignment
- Preference alignment algorithms including PPO, DPO, KTO, and ORPO for RLHF-style training
- Flexible computation precision from 16-bit full-parameter tuning down to 2-bit QLoRA for consumer hardware
- Acceleration operators like FlashAttention-2 and Unsloth integrated for efficient training throughput
Use Cases
- Experimenting with different fine-tuning strategies and optimization algorithms on cutting-edge models
- Adapting foundation models for domain-specific tasks such as code generation, customer support, and content creation
- Non-engineers customizing model behavior through the no-code interface without writing training scripts
- Rapid iteration on model alignment and evaluation with built-in monitoring and benchmarking tools
Technical Highlights
- Supports inference through both Transformers and vLLM backends for flexible deployment
- Integrates experiment monitoring tools including LlamaBoard, TensorBoard, Wandb, MLflow, and SwanLab
- Built-in optimization algorithms such as GaLore, DoRA, LongLoRA, and PiSSA
- Quantization methods including AQLM, AWQ, GPTQ, and HQQ enable efficient training on consumer hardware