Qwen2.5-VL-7B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Model Type | Vision-Language Model |
| Architecture | Transformer-based with Dynamic Resolution Training |
| Author | Qwen Team |
| Model URL | Hugging Face: Qwen/Qwen2.5-VL-7B-Instruct |
What is Qwen2.5-VL-7B-Instruct?
Qwen2.5-VL-7B-Instruct is a state-of-the-art vision-language model that represents a significant advancement in multimodal AI capabilities. This instruction-tuned model excels at understanding and processing various visual inputs, including images, videos, charts, and structured documents.
Implementation Details
The model implements several architectural innovations, including dynamic resolution and frame rate training for video understanding, and a streamlined vision encoder with window attention. It supports context lengths up to 32,768 tokens and can be extended using YaRN for longer sequences.
- Enhanced ViT architecture with SwiGLU and RMSNorm optimizations
- Dynamic FPS sampling with mRoPE temporal alignment
- Flexible resolution handling with configurable pixel ranges
Core Capabilities
- Advanced visual understanding of objects, texts, charts, and layouts
- Long video comprehension (over 1 hour) with event capture
- Precise object localization through bounding boxes and points
- Structured output generation for documents and forms
- Agent-like capabilities for computer and phone interaction
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle multiple input types, from images to hour-long videos, combined with its structured output capabilities and agent-like behavior, sets it apart from traditional vision-language models. Its performance across various benchmarks demonstrates superior capabilities in visual understanding and temporal reasoning.
Q: What are the recommended use cases?
The model excels in document analysis, video understanding, visual QA tasks, and structured data extraction. It's particularly suitable for applications in finance, commerce, and any scenario requiring detailed visual analysis or temporal understanding.
