Quen 2.5VL: A Powerful Vision Model for Local Agentic Tasks

Quen 2.5VL: A Powerful Vision Model for Local Agentic Tasks

Quen has introduced its latest model, Quen 2.5VL, expanding its capabilities in the realm of vision-based AI. This new model distinguishes itself through its capacity for document parsing, precise object grounding, ultra-long video understanding, fine-grained video grounding and enhanced agent functionality for computers and mobile devices. But what makes this release noteworthy, and how does it compare to existing solutions?

Key Features and Capabilities

Quen 2.5VL encompasses a range of models, including 3 billion, 7 billion, and 72 billion parameter variants, which allows the model to be run locally.

• Document Parsing: The model is capable of parsing documents, potentially enabling enhanced OCR and information extraction processes.
• Object Grounding: It also offers precise object grounding across various formats, allowing for more accurate identification and localization of objects within images and videos.
• Video Understanding: Notably, Quen 2.5VL introduces video understanding capabilities, allowing it to analyze and interpret video content. This includes ultra-long and fine-grained analysis.
• Agentic Functionality: The model can perform agentic tasks, meaning it can control computers and mobile devices with pinpoint accuracy, similar to OpenAI's operator.

Performance and Benchmarks

According to benchmarks, Quen 2.5VL demonstrates significantly better performance than GPT-4-0 and Sonnet, particularly in computer use tasks. This suggests a strong potential for applications requiring automated interaction with computer interfaces. How will these benchmarks translate to real-world applications and diverse use cases?

Local Deployment and Open Source Implementation

One of the key advantages of Quen 2.5VL is the ability to run it locally. While native support for Olama and VLLM is still in progress, an open-source implementation is available that serves the model as an OpenAI-compatible API. This allows developers to integrate Quen 2.5VL into their projects with relative ease, either via Docker or local setup. The process involves cloning the repository, downloading the desired model (defaulting to the 7 billion parameter version), and installing the necessary dependencies.

Practical Applications: Browser Automation

Quen 2.5VL can be used for browser automation tasks. By utilizing a browser use web UI, users can configure the model to perform specific actions within a browser environment. The setup involves selecting the OpenAI option in the LLM configuration, entering the model name (quen 2.5vl) and API base URL, and enabling the vision option in the agent settings. From there, the model can be instructed to perform tasks such as searching for information on Google or booking flights, demonstrating its ability to interact with and interpret visual elements on a webpage.

Conclusion

Quen 2.5VL represents a notable advancement in vision-based AI, particularly with its focus on local deployment and agentic capabilities. Its ability to perform document parsing, object grounding, and video understanding opens up possibilities for various applications. As support for VLLM and OLAMA continues to develop, the accessibility and usability of Quen 2.5VL are poised to improve further. Which new applications will emerge as developers explore the potential of this model?

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