MiniGPT-v2 is a multimodal model that efficiently handles various vision-language tasks using straightforward multi-modal instructions, demonstrating remarkable performance across numerous tasks. The model's architecture comprises three main components: Visual Backbone, Linear Projection Layer, and Large Language Model. The visual backbone is inspired by the Vision Transformer (ViT) and serves as the model's vision encoder, while the linear projection layer reduces the number of visual input tokens to process high-quality images efficiently. The large language model comes from LLaMA-2 and acts as a single interface for different vision-language inputs, enabling MiniGPT-v2 to perform a wide range of vision-language tasks with versatility. MiniGPT-v2 has surpassed its predecessor, MiniGPT-4, in performance and capabilities within the domain of vision-language multi-task learning, showcasing consistent performance that firmly established its position at the forefront of state-of-the-art models.