LLaMA-Factory open source analysis

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

Project overview

⭐ 63329 · Python · Last activity on GitHub: 2025-11-30

GitHub: https://github.com/hiyouga/LLaMA-Factory

Why it matters for engineering teams

LLaMA-Factory addresses the challenge of efficiently fine-tuning large language models (LLMs) and vision-language models (VLMs) across a wide range of architectures, supporting over 100 models. This open source tool for engineering teams simplifies the complex process of adapting pre-trained models to specific tasks, reducing both time and computational resources. It is particularly suited for machine learning and AI engineering teams who require a production ready solution for scalable model tuning. The project is mature enough for production use, benefiting from active development and community support. However, it may not be the best choice for teams seeking a lightweight or minimalistic fine-tuning framework, or those working with models outside the supported ecosystem where custom solutions might be more appropriate.

When to use this project

LLaMA-Factory is a strong choice when your team needs a unified, efficient approach to fine-tuning a variety of large language and vision models, especially in production environments. Teams should consider alternatives if they require specialised fine-tuning for niche models or prefer a simpler, less resource-intensive setup.

Team fit and typical use cases

Machine learning engineers and AI specialists benefit most from LLaMA-Factory, using it to fine-tune models for tasks like natural language processing, instruction tuning, and reinforcement learning with human feedback. It commonly appears in products requiring custom AI capabilities, such as chatbots, recommendation systems, and automated content generation, offering a self hosted option for scalable and adaptable model training.

Best suited for

Machine Learning and AI Engineer

Topics and ecosystem

agent ai deepseek fine-tuning gemma gpt instruction-tuning large-language-models llama llama3 llm lora moe nlp peft qlora quantization qwen rlhf transformers

Activity and freshness

Latest commit on GitHub: 2025-11-30. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.