Note: this model requires Ollama 0.7.0. Download Ollama
Qwen2.5-VL, the new flagship vision-language model of Qwen and also a significant leap from the previous Qwen2-VL.
The key features include:
Understand things visually: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
Being agentic: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.
Capable of visual localization in different formats: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.
Generating structured outputs: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.
Performance
We evaluate our models with the SOTA models as well as the best models of similar model sizes. In terms of the flagship model Qwen2.5-VL-72B-Instruct, it achieves competitive performance in a series of benchmarks covering domains and tasks, including college-level problems, math, document understanding, general question answering, math, and visual agent. Notably, Qwen2.5-VL achieves significant advantages in understanding documents and diagrams, and it is capable of playing as a visual agent without task-specific fine tuning.
In terms of smaller models, Qwen2.5-VL-7B-Instruct outperforms GPT-4o-mini in a number of tasks, and Qwen2.5-VL-3B, which is a solution for edge AI, even outperforms the 7B model of our previous version Qwen2-VL.