Qwen2.5-VL-3B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 3 Billion |
| Model Type | Vision-Language Model |
| Architecture | Transformer-based with Dynamic Resolution and Frame Rate Training |
| Model URL | https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct |
What is Qwen2.5-VL-3B-Instruct?
Qwen2.5-VL-3B-Instruct is an advanced vision-language model that represents a significant evolution in multimodal AI. Built upon the success of Qwen2-VL, this instruction-tuned model combines sophisticated visual understanding with powerful language processing capabilities.
Implementation Details
The model features a streamlined vision encoder with optimized window attention and implements dynamic resolution training for both spatial and temporal dimensions. It utilizes mRoPE with IDs and absolute time alignment for enhanced temporal understanding, and supports context lengths up to 32,768 tokens.
- Optimized ViT architecture with SwiGLU and RMSNorm
- Dynamic FPS sampling for variable video frame rates
- Flexible resolution support with configurable pixel ranges
- Enhanced temporal sequence learning capabilities
Core Capabilities
- Advanced visual recognition of objects, texts, charts, and layouts
- Long video understanding (1+ hour) with event detection
- UI interaction and computer/phone use comprehension
- Structured data extraction from documents and forms
- Precise object localization with bounding box generation
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle multiple visual formats, long videos, and structured outputs, combined with its optimized architecture for both performance and efficiency, sets it apart from other vision-language models.
Q: What are the recommended use cases?
The model excels in document analysis, video content understanding, UI automation, and general visual-language tasks. It's particularly suitable for applications requiring structured data extraction from visual inputs.
