Comparison of Frameworks for Fine-Tuning AI: Unsloth, Axolotl, TRL, and LLaMA-Factory — Speed, VRAM, and Multi-GPU Support

The article compares four frameworks for fine-tuning large language models: Unsloth, Axolotl, TRL (from Hugging Face), and LLaMA-Factory. Researchers tested them on fine-tuning tasks, measuring training speed and video memory (VRAM) usage on a single GPU, as well as performance when distributing training across multiple GPUs. Each framework showed its strengths and weaknesses: Unsloth is likely optimized for speed, Axolotl is known for flexibility, TRL is tightly integrated with the Hugging Face ecosystem, and LLaMA-Factory offers a user-friendly interface. Choosing the right tool depends on specific project requirements, such as available resources and the need to work with multiple GPUs.