LLaMA-Factory open source analysis

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

Project overview

⭐ 62541 · Python · Last activity on GitHub: 2025-11-13

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

Why it matters for engineering teams

LLaMA-Factory addresses the practical challenge of efficiently fine-tuning a wide range of large language models (LLMs) and vision-language models (VLMs) within a unified framework. This open source tool for engineering teams is particularly suited for machine learning and AI engineers who need to customise models for specific tasks without extensive resource overhead. It offers a production ready solution that supports over 100 models, ensuring flexibility and scalability in real-world applications. While mature and reliable for many fine-tuning scenarios, it may not be the best choice for teams seeking a lightweight or highly specialised approach focused solely on inference or deployment rather than training.

When to use this project

LLaMA-Factory is a strong choice when your team requires a consistent and efficient fine-tuning pipeline for multiple large models, especially in research or product development environments. Teams should consider alternatives if their primary need is minimal setup for inference or if they require a fully managed cloud service instead of a self hosted option for model tuning.

Team fit and typical use cases

Machine learning engineers and AI specialists benefit most from LLaMA-Factory by integrating it into workflows that demand customisation of LLMs and VLMs for NLP, instruction tuning, or reinforcement learning tasks. It typically appears in products involving natural language understanding, chatbots, or AI agents where tailored model behaviour is critical. The project supports teams looking for a self hosted option to maintain control over fine-tuning processes and model versions.

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: 2025-11-13. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.