Qwen2.5 VL 3B

Qwen2.5-VL-3B is a vision-language model from the Qwen family developed by Alibaba Cloud, representing a significant advancement in multimodal AI capabilities. The model features an innovative architecture that builds upon its predecessors through several key technological improvements detailed in the research paper.

The model's architecture incorporates dynamic resolution and frame rate training for video understanding, extending previous capabilities to the temporal dimension through dynamic FPS sampling. This enhancement enables the model to process videos at various sampling rates effectively. The implementation includes updated mRoPE (Multimodal Rotary Position Embedding) in the time dimension, utilizing IDs and absolute time alignment to better understand temporal sequences and speed.

A streamlined vision encoder utilizing window attention has been implemented, improving both training and inference speeds. The architecture also incorporates SwiGLU and RMSNorm structures for better alignment with the base language model components. These architectural choices make the 3B variant particularly suitable for edge AI applications, while maintaining competitive performance against larger models.

Capabilities and Features

Qwen2.5-VL-3B demonstrates several advanced capabilities that set it apart from previous models:

1. Enhanced Visual Understanding

   • Advanced object recognition
   • Sophisticated analysis of charts and layouts
   • Improved text recognition within images
   • Comprehensive document parsing capabilities

2. Video Processing

   • Long-video comprehension (exceeding 1 hour)
   • Precise temporal event localization
   • Dynamic frame rate processing
   • Unified image and video handling paradigm

3. Agent Functionality

   • Computer and phone interface interaction
   • Advanced reasoning capabilities
   • Dynamic tool direction
   • Structured output generation

4. Document Processing

   • Omnidocument parsing across multiple scenarios
   • Multilingual content handling
   • Support for various document types including:
     • Handwriting
     • Tables
     • Charts
     • Chemical formulas
     • Music sheets

Performance and Benchmarks

The 3B variant shows impressive performance across various benchmarks, often competing effectively with larger models. Notable benchmark results include strong showings on:

• MMMU-Pro
• DocVQA
• InfoVQA
• MathVista-testmini
• ScreenSpot
• AITZ_EM
• Android Control High_EM
• AndroidWorld_SR
• MobileMiniWob++

While the larger 7B and 72B variants generally outperform the 3B model, the performance gap is surprisingly narrow in many tasks, making the 3B variant an efficient choice for resource-constrained applications.

Technical Specifications and Usage

For optimal performance, users can adjust image resolution using min_pixels and max_pixels parameters to balance performance and computational costs. The model supports processing of texts exceeding 32,768 tokens, though using YaRN for length extrapolation is not recommended due to its impact on localization tasks.

The model uses Safetensors for parameter storage and is released under the qwen-research license. It is compatible with both the Hugging Face Transformers library and ModelScope, supporting various input formats including:

• Local image files
• Base64 encoded images
• Image URLs
• Local video files

Reference Links

1. Qwen 2.5-VL Blog Post - Official blog post detailing the model family
2. Qwen 2.5-VL GitHub - Source code and implementation details
3. HuggingFace Model Page - Model downloads and documentation
4. Research Paper - Technical details and methodology
5. YaRN Paper - Information about YaRN technique
6. Decord Installation Guide - Video processing library setup
7. Qwen-VL Paper - Background on previous model version