LLaMA-Adapter provides a lightweight adapter-based approach for fine-tuning LLaMA models to follow instructions and support multi-modal inputs. It introduces zero-init attention mechanisms to efficiently adapt pre-trained language models with minimal additional parameters. The project includes both instruction-following and visual instruction models, with V2 extending to multi-modal reasoning.