Qwen2.5-VL 72B represents a significant advancement in vision-language modeling, building upon its predecessor Qwen2-VL. Released in January 2025, it demonstrates enhanced capabilities across various visual understanding tasks while introducing architectural improvements for better efficiency.
Architecture and Technical Innovations
The model features a streamlined architecture that includes several key innovations. At its core is a novel dynamic resolution Vision Transformer (ViT) visual encoder, designed for optimal efficiency through Window Attention (limiting Full Attention to four layers) and architectural alignment with LLMs using RMSNorm and SwiGLU. The video encoder implements dynamic FPS sampling, extending dynamic resolution capabilities to the temporal dimension, enabling the model to process videos at varying frame rates and better understand temporal sequences.
The architecture incorporates Multimodal Rotary Position Embedding (M-RoPE) for improved fusion of positional information across modalities. For parameter storage, the model utilizes Safetensors format, containing 73.4B parameters in BF16 format. The official blog post details how these architectural choices contribute to both training and inference efficiency.
Capabilities and Performance
Qwen2.5-VL 72B demonstrates exceptional capabilities across multiple domains:
- Visual Understanding: Enhanced ability to analyze objects, charts, layouts, and various visual formats
- Document Processing: Advanced text recognition across multiple languages and scenarios, including handwriting, tables, and complex layouts
- Video Comprehension: Ability to process videos over an hour long with precise event localization
- Agent Capabilities: Sophisticated reasoning and tool use for computer and phone operations
- Structured Output Generation: Precise generation of tables, forms, and invoices
- Object Grounding: Accurate bounding box and point-based representations with standardized JSON outputs
The model shows strong performance across numerous benchmarks, often outperforming its predecessor Qwen2-VL and competing with leading models like GPT-4o and Claude3.5 Sonnet. Notable benchmark results span image understanding (MMMU, MathVista, DocVQA), video comprehension (VideoMME, MMBench-Video), and agent tasks (ScreenSpot, Android Control).
Model Family and Variants
The Qwen2.5-VL family includes three parameter sizes:
- 3B parameters: Suitable for edge AI applications, outperforming the 7B Qwen2-VL model
- 7B parameters: Surpasses GPT-4o-mini in several areas
- 72B parameters: The flagship model, achieving state-of-the-art performance across most benchmarks
All variants were trained on approximately 18 trillion tokens, a significant increase from the 7 trillion tokens used in earlier versions. This expanded training contributes to improved reasoning, common sense, and expertise across the model family.
Usage and Implementation
The model is integrated into the Hugging Face Transformers library and can be installed using:
pip install git+https://github.com/huggingface/transformers accelerate
For handling various visual inputs, the qwen-vl-utils package is recommended:
pip install qwen-vl-utils[decord]
The model supports YaRN for efficient context window extension, though this may affect localization tasks. For long videos, increasing max_position_embeddings is recommended.
References
- Qwen2.5 VL Blog - Official blog post detailing model architecture and capabilities
- Qwen2.5 VL GitHub - Source code and implementation details
- Hugging Face Model - Model weights and documentation
- Qwen2-VL Research Paper - Technical details and methodology
- YaRN Paper - Context window extension technique
- Decord Installation - Video processing library setup
