R-4B is a multimodal large language model that enhances general-purpose auto-thinking by dynamically switching between thinking and non-thinking modes based on task complexity. It employs a two-stage training approach to improve response efficiency and reduce computational costs, achieving state-of-the-art performance among similar models. The model is open-source and offers user control over its thinking capabilities.