LLaMA-Factory

This project targets the complexity of fine-tuning over 100 large language and vision-language models through a unified interface, combining zero-code CLI and a web UI built on Gradio. It supports diverse hardware backends including AMD GPUs and Ascend NPUs, and integrates with cloud platforms like Colab and Alibaba PAI-DSW, which can simplify experimentation and deployment for ML engineers. With over 64,000 stars and recent commits, it shows strong community interest and active maintenance, though its growth remains stable rather than explosive. The main users are ML engineers and researchers looking to rapidly prototype or deploy fine-tuned models without building custom pipelines. It is less suitable for teams requiring highly custom fine-tuning workflows or those prioritising minimal dependencies and lightweight tooling, where lower-level frameworks like Hugging Face Transformers or PEFT might be more appropriate.

Choose this when you want a broad, ready-to-run framework for fine-tuning many LLMs and VLMs with minimal coding and multi-backend support. If your focus is on highly custom fine-tuning scripts or minimal infrastructure overhead, consider Hugging Face Transformers or PEFT instead, which offer more granular control and lighter dependencies.

Typically used in teams building internal NLP or multimodal applications that require adapting large models to domain-specific data, such as document understanding pipelines or chatbots enhanced with visual inputs. It suits environments where integration with diverse hardware and cloud platforms is necessary for scaling training and deployment.

Latest commit: 2025-12-30. Activity data is based on daily RepoPi snapshots of the GitHub repository.