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Project Description: LLaMA Factory

What is the project about?

LLaMA Factory is a framework for easily fine-tuning a wide variety of large language models (LLMs). It offers a unified interface for training, evaluation, and deployment, supporting various models, training methods, and datasets.

What problem does it solve?

It simplifies the complex process of fine-tuning LLMs, making it accessible to users with limited coding experience. It removes the need to write custom code for different models and training techniques, providing a streamlined and efficient workflow. It also addresses the challenge of resource constraints by offering methods like LoRA and QLoRA for efficient fine-tuning on limited hardware.

What are the features of the project?

  • Wide Model Support: Supports 100+ LLMs, including LLaMA, Mistral, Qwen, ChatGLM, and many others.
  • Multiple Training Methods: Supports pre-training, supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, and SimPO.
  • Resource Efficiency: Offers full-tuning, freeze-tuning, LoRA, and QLoRA (quantized LoRA) for various hardware setups.
  • Advanced Algorithms: Includes GaLore, BAdam, APOLLO, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, and PiSSA.
  • Practical Tricks: Implements FlashAttention-2, Unsloth, Liger Kernel, RoPE scaling, NEFTune, and rsLoRA.
  • Diverse Tasks: Supports multi-turn dialogue, tool usage, image understanding, visual grounding, video recognition, audio understanding, etc.
  • Experiment Monitoring: Integrates with LlamaBoard, TensorBoard, Wandb, MLflow, and SwanLab.
  • Fast Inference: Provides OpenAI-style API and Gradio UI, with vLLM integration for accelerated inference.
  • Day-N Support: Provides rapid support (often within a day) for cutting-edge models.
  • Easy to use: Zero-code CLI and Web UI.

What are the technologies used in the project?

  • Python
  • PyTorch
  • Transformers (Hugging Face)
  • Datasets (Hugging Face)
  • Accelerate
  • PEFT (Parameter-Efficient Fine-Tuning)
  • TRL (Transformer Reinforcement Learning)
  • Optional: DeepSpeed, bitsandbytes, vLLM, FlashAttention-2, AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
  • Gradio (for Web UI)
  • Docker (optional, for containerization)

What are the benefits of the project?

  • Accessibility: Simplifies LLM fine-tuning for users with varying levels of expertise.
  • Efficiency: Provides methods for training large models on limited resources.
  • Flexibility: Supports a wide range of models, tasks, and training approaches.
  • Speed: Offers faster training and inference through optimized techniques.
  • Reproducibility: Facilitates reproducible research and development.
  • Community Support: Active community and frequent updates.

What are the use cases of the project?

  • Research: Prototyping and experimenting with new LLM architectures and training methods.
  • Development: Building custom LLM-powered applications for specific tasks.
  • Education: Learning about LLM fine-tuning and related techniques.
  • Domain Adaptation: Adapting pre-trained LLMs to specific domains or tasks (e.g., legal, medical, financial).
  • Chatbot Development: Creating specialized chatbots with tailored knowledge and conversational styles.
  • Content Generation: Fine-tuning models for specific writing styles or content types.
  • Multimodal Applications: Training models that combine text with other modalities like images or audio.
LLaMA-Factory screenshot