Youtu-VL is a compact Vision-Language Model (VLM) developed by Tencent with 4 billion parameters. It introduces a new framework called Vision-Language Unified Autoregressive Supervision (VLUAS). This approach enhances the model’s ability to understand visual data and language simultaneously, allowing it to tackle vision tasks without needing modifications or additional components. It performs well across various benchmarks, showcasing its capability in both vision-centric and general multimodal tasks. In vision-centric tasks, Youtu-VL excels in areas like visual grounding, image classification, and object detection. Its design treats image and text tokens equally, which enables it to predict visual outputs alongside text-based predictions. This versatility means users can deploy a single model for diverse applications, from depth estimation to human pose estimation, without the need for specialized configurations. Despite its smaller size compared to other models, Youtu-VL achieves competitive results in general multimodal tasks such as visual question answering and optical character recognition. The model’s architecture allows it to efficiently process a wide array of inputs, making it suitable for real-world applications. Users can interact with the model easily through provided code snippets and demos, enhancing its accessibility for developers and researchers. The article also includes practical guidance on setting up the model in a Python environment, alongside examples of tasks like object detection and referring segmentation. For those interested in further exploration, it suggests citing the related research papers, emphasizing the model's potential contributions to the field.