This is a simplified guide to an AI model called Youtu-VL-4B-Instruct maintained by tencent. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter. This is a simplified guide to an AI model called Youtu-VL-4B-Instruct Youtu-VL-4B-Instruct maintained by tencent tencent . If you like these kinds of analysis, join AIModels.fyi AIModels.fyi or follow us on Twitter Twitter . Model overview Youtu-VL-4B-Instruct is a lightweight vision-language model from Tencent built on the Youtu-LLM architecture with 4 billion parameters. The model introduces Vision-Language Unified Autoregressive Supervision (VLUAS), a training approach that strengthens visual perception and multimodal understanding by treating visual signals as autoregressive targets rather than passive inputs. This enables the model to handle both vision-centric tasks like segmentation and depth estimation alongside traditional vision-language tasks, all within a single unified architecture. Youtu-VL-4B-Instruct Tencent Compared to other efficient vision-language models like Yi-VL-6B and Kimi-VL-A3B-Instruct, Youtu-VL-4B-Instruct achieves competitive performance across benchmarks while maintaining a compact footprint that facilitates efficient deployment. Yi-VL-6B Kimi-VL-A3B-Instruct Youtu-VL-4B-Instruct Model inputs and outputs The model accepts images and text prompts as input and generates text responses that can include detailed visual descriptions, answers to questions about images, or predictions for vision-centric tasks. The architecture processes both image and text tokens through a unified autoregressive framework, allowing flexible output formats depending on the task. Inputs Images: Visual content in standard formats for analysis and understandingText prompts: Questions, instructions, or requests about the provided imagesChat messages: Multi-turn conversation format with role-based exchanges Images: Visual content in standard formats for analysis and understanding Images Text prompts: Questions, instructions, or requests about the provided images Text prompts Chat messages: Multi-turn conversation format with role-based exchanges Chat messages Outputs Text responses: Detailed descriptions, answers, or predictions in natural languageVisual predictions: Dense predictions for tasks like segmentation masks, depth maps, or coordinate outputs for grounding and detection tasksMulti-token generations: Extended outputs up to 32,768 tokens for comprehensive analysis Text responses: Detailed descriptions, answers, or predictions in natural language Text responses Visual predictions: Dense predictions for tasks like segmentation masks, depth maps, or coordinate outputs for grounding and detection tasks Visual predictions Multi-token generations: Extended outputs up to 32,768 tokens for comprehensive analysis Multi-token generations Capabilities The model demonstrates strong performance across vision-centric tasks including visual grounding, image classification, object detection, referring segmentation, semantic segmentation, depth estimation, object counting, and human pose estimation. For general multimodal tasks, it handles visual question answering, multimodal reasoning, mathematics, optical character recognition, multi-image understanding, and GUI agent interactions. The VLUAS approach enables these capabilities by jointly reconstructing visual tokens and text, which preserves dense visual information while strengthening multimodal semantic understanding without requiring task-specific architectural modifications. What can I use it for? Youtu-VL-4B-Instruct suits projects requiring efficient vision-language understanding on resource-constrained systems. Applications include building visual search engines, implementing interactive image analysis tools, creating accessibility features that describe images to users, developing autonomous document processing systems for OCR and form understanding, and powering GUI-based automation agents. The model's compact size makes it practical for edge deployment, mobile applications, or cost-sensitive cloud services where larger models become impractical. The versatility across both vision-centric and general multimodal tasks makes it suitable for research prototyping or production systems requiring a single unified model rather than multiple task-specific solutions. Youtu-VL-4B-Instruct Things to try Experiment with the model's dense prediction capabilities by testing it on segmentation, depth estimation, and pose estimation tasks alongside traditional visual question answering. The unified architecture means a single prompt can request multiple types of outputs—try asking the model to identify objects while simultaneously predicting depth or segmentation masks. Test multi-image understanding by providing multiple images in sequence and asking comparative questions. Since the model processes long contexts, try feeding it screenshots of complex interfaces and asking it to navigate or summarize what it sees. Compare performance on vision-centric tasks against general multimodal benchmarks to understand where the VLUAS training provides the most benefit relative to text-focused vision-language models.

Meet Youtu-VL-4B: Tencent’s Tiny Model That Does Segmentation, Depth, and VQA

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