Unsloth is an open-source toolkit for making large language model fine-tuning faster and more memory-efficient. It provides optimized training workflows for popular open models and techniques such as LoRA and QLoRA, helping users fine-tune models on more modest GPU hardware or reduce the cost and time required on larger systems. The project focuses on practical performance improvements while remaining compatible with common machine-learning ecosystems.

Unsloth includes notebooks, training utilities, model support, and workflows for supervised fine-tuning and reinforcement-learning-style post-training. It is widely used by developers and researchers customizing open LLMs for domain-specific tasks, instruction following, reasoning, or experimentation. Rather than being an end-user AI application, Unsloth sits in the model-development layer and is best suited to people who need an accessible path to efficient fine-tuning without building a heavily optimized training stack themselves.