In the landscape of continuous development of artificial intelligence, integration of vision and language ability is still a complex challenge. Traditional models often struggle on tasks that require a minor understanding of vision and text data, which leads to the limitation of applications, such as image analysis, video understanding and interactive tools. These challenges emphasize the demand for more complex visual language models, which can seamlessly interpret and respond to multi -mode information seamlessly.
Qwen AI has launched Qwen2.5-VL, which is a new visual language model designed to handle the computer-based task that is set at least. On the basis of its predecessor Qwen2-VL, this iterative iteration provides improved visual understanding and reasoning functions. Qwen2.5-VL can identify a series of objects, from daily items such as flowers and birds to more complex visual elements, such as text, charts, icons, and layouts. In addition, it can act as a smart visual assistant to explain and interact with software tools on computers and mobile phones without having to be widely customized.
From a technical point of view, Qwen2.5-VL combines several progress. It uses the visual transformer (VIT) structure to complete the SWIGLU and RMSNORM, and keeps its structure consistent with the Qwen2.5 language model. This model supports dynamic resolution and adaptive frame rate training, thereby enhancing its ability to effectively process video. By using dynamic frame sampling, it can understand the time sequence and movement, thereby improving its ability to recognize the content of the video content. These enhanced functions make their vision coding more effective, thereby optimizing training and reasoning speed.
Performance assessment shows that Qwen2.5-VL-72B-Instrument has achieved strong results within the cross-basis range of multiple benchmarks including mathematics, document understanding, general question answers, and video analysis. It performs well in processing documents and charts, and effectively runs as a visual assistant, without fine -tuning in mission. The smaller models in the Qwen2.5-VL family also show competitive performance. Qwen2.5-VL-7B teaching method surpasses GPT-4O-mini in a specific task, while Qwen2.5-VL-3B is better than the previous 7B of 7B The version of Qwen2 -VL makes it an eye -catching option for the limited environment.
All in all, Qwen2.5-VL proposed a delicate visual modeling method to solve the previous limitations by improving visual understanding and interactive ability. It has the ability to perform tasks on computers and mobile devices without extensive settings to make it a practical tool in real applications. With the continuous development of AI, models such as Qwen2.5-VL paved the road for more seamless and intuitive multi-mode interactions, thereby facing the gap between visual and text intelligence.
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Qwen2.5-VL released by Qwen AI: The powerful visual language model for seamless computer interaction first appeared on MarkTechPost.
