Qwen2-VL-72B
Q
Qwen2 VL 72B
Overview :
Qwen2-VL-72B is the latest iteration of the Qwen-VL model, reflecting nearly a year of innovative advancements. This model has achieved state-of-the-art performance in visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, and more. It can comprehend videos exceeding 20 minutes and can be integrated into devices such as smartphones and robots for automated operations based on visual contexts and text instructions. In addition to English and Chinese, Qwen2-VL now supports understanding textual content in various languages found in images, including most European languages, Japanese, Korean, Arabic, Vietnamese, and others. Model architecture updates include Naive Dynamic Resolution and Multimodal Rotary Position Embedding (M-ROPE), enhancing its multimodal processing capabilities.
Target Users :
The target audience for Qwen2-VL-72B includes researchers, developers, and enterprises in need of a powerful visual language model for image and video understanding tasks. Its multilingual support and multimodal processing capabilities make it an ideal choice for users worldwide, especially in scenarios requiring comprehension and manipulation of visual information.
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Use Cases
Using Qwen2-VL-72B for image recognition and solving mathematical problems
Developing content creation and Q&A systems within long videos
Integrating into robots for automated navigation and operations based on visual commands
Features
Supports image understanding across various resolutions and aspect ratios
Capable of comprehending videos longer than 20 minutes for high-quality video Q&A, dialogue, content creation, and more
Can be integrated into mobile devices and robots to achieve automated operations based on visual contexts and text instructions
Supports understanding of multilingual text, including European languages, Japanese, Korean, Arabic, Vietnamese, and more
Naive Dynamic Resolution allows processing of images at any resolution, providing a more human-like visual processing experience
Multimodal Rotary Position Embedding (M-ROPE) enhances the ability to process positional information across 1D text, 2D visuals, and 3D videos
How to Use
1. Install the latest version of the Hugging Face transformers library using the command: pip install -U transformers
2. Visit the Qwen2-VL-72B Hugging Face page for model details and usage guidelines
3. Download the model files as needed, and load the model in a local or cloud environment
4. Input images or videos into the model and obtain the model's output results
5. Post-process the model output according to your application scenario, such as text generation or question answering
6. Participate in community discussions to gain technical support and best practices
7. If necessary, further fine-tune the model to fit specific application requirements
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