Qwen2.5-VL-7B-Instruct-AWQ
| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Model Type | Vision-Language Model |
| Architecture | Transformer-based with AWQ quantization |
| Model URL | Qwen/Qwen2.5-VL-7B-Instruct-AWQ |
| Author | Qwen Team |
What is Qwen2.5-VL-7B-Instruct-AWQ?
Qwen2.5-VL-7B-Instruct-AWQ is an advanced vision-language model that represents a significant evolution in multimodal AI. This quantized version maintains high performance while offering improved efficiency and reduced memory footprint through AWQ quantization. The model excels in understanding various visual inputs, from common objects to complex charts and layouts, while supporting extensive video analysis capabilities.
Implementation Details
The model features a streamlined vision encoder with strategic window attention implementation in ViT, enhanced with SwiGLU and RMSNorm optimizations. It employs dynamic resolution and frame rate training for video understanding, with mRoPE temporal dimension updates enabling precise moment identification in videos.
- Supports context length up to 32,768 tokens
- Implements YaRN for enhanced model length extrapolation
- Flexible input resolution support with configurable pixel ranges
- Integrated with both ModelScope and Hugging Face frameworks
Core Capabilities
- Advanced visual recognition of objects, texts, charts, and layouts
- Long video comprehension (over 1 hour) with event capturing
- Visual localization through bounding boxes and points
- Structured output generation for financial and commercial documents
- Interactive agent capabilities for computer and phone use scenarios
Frequently Asked Questions
Q: What makes this model unique?
The model combines sophisticated visual understanding with efficient quantization, offering high-performance capabilities while maintaining reasonable computational requirements. Its ability to handle long videos and provide structured outputs sets it apart from conventional vision-language models.
Q: What are the recommended use cases?
The model excels in document analysis, visual data extraction, long video understanding, and interactive visual tasks. It's particularly suitable for applications in finance, commerce, and scenarios requiring detailed visual analysis with structured output generation.
