Qwen2-VL-7B

Qwen2-VL-7B is the latest visual language model that supports multimodal understanding and text generation.

CommonProductImageVisual Language ModelMultimodal

Qwen2-VL-7B is the latest iteration of the Qwen-VL model, representing a year of innovative advancements. It achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, among others. The model can comprehend videos over 20 minutes long, providing high-quality support for video-based question answering, dialogue, and content creation. Additionally, Qwen2-VL supports multiple languages, including English, Chinese, and most European languages, as well as Japanese, Korean, Arabic, Vietnamese, and more. Updates to the model architecture include Naive Dynamic Resolution and Multimodal Rotary Position Embedding (M-ROPE), enhancing its multimodal processing capabilities.

- Supports image understanding at various resolutions and aspect ratios: Qwen2-VL has achieved state-of-the-art performance on visual understanding benchmarks.
- Understands videos over 20 minutes long: Qwen2-VL can comprehend long videos
enabling high-quality video question answering and dialogue.
- Integrates into devices like mobile phones and robots: Qwen2-VL has complex reasoning and decision-making capabilities that can be integrated into mobile devices and robots for automated operations based on visual environments and text instructions.
- Multilingual support: Qwen2-VL offers text understanding in various languages
including most European languages
Japanese
Korean
Arabic
Vietnamese

\The target audience for Qwen2-VL-7B includes researchers
developers
and enterprise users
especially those engaged in visual language understanding and text generation. This model can be applied in various scenarios
such as automated content creation

Example 1: Using Qwen2-VL-7B for automated summarization and question answering of video content.
Example 2: Integrating Qwen2-VL-7B into mobile applications for image-based search and recommendations.
Example 3: Utilizing Qwen2-VL-7B for visual question answering and content analysis of multilingual documents.

1. Install the latest version of the Hugging Face Transformers library using the command `pip install -U transformers`.
2. Visit the Qwen2-VL-7B page on Hugging Face to learn more about the model and access usage guidelines.
3. Select the appropriate pre-trained model based on your specific needs for download and deployment.
4. Use the tools and interfaces provided by Hugging Face to integrate Qwen2-VL-7B into your project.
5. Write code per the model's API documentation to handle image and text inputs.

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Qwen2-VL-7B Visit Over Time

Monthly Visits

20899836

Bounce Rate

46.04%

Page per Visit

5.2

Visit Duration

00:04:57

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