Qwen2-VL-72B-Instruct

Qwen2-VL-72B-Instruct is an advanced image-text-to-text model designed for a wide range of visual understanding and reasoning tasks. This model is a significant upgrade from the previous Qwen-VL, incorporating several key enhancement.

Key Features of Qwen2-VL-72B-Instruct

• Superior Image Understanding: Qwen2-VL achieves state-of-the-art performance on various visual understanding benchmarks including MathVista, DocVQA, RealWorldQA, and MTVQA. It demonstrates strong capabilities in processing images with different resolutions and aspect ratios.

• Agent Capabilities: Qwen2-VL can be integrated with devices like mobile phones and robots for automatic operation based on visual environment and text instructions, demonstrating complex reasoning and decision-making skills.

• Multilingual Support: Beyond English and Chinese, the model supports understanding text within images in many languages, including most European languages, Japanese, Korean, Arabic, and Vietnamese.

• Dynamic Resolution Handling: Qwen2-VL can handle arbitrary image resolutions, mapping them into a dynamic number of visual tokens for a more human-like visual processing experience.

• Advanced Positional Embedding: The model uses Multimodal Rotary Position Embedding (M-ROPE) to capture 1D textual, 2D visual, and 3D video positional information, enhancing its multimodal processing capabilities

Technical Specifications

• Model Architecture: The model employs a large-scale transformer architecture with 72 billion parameters.

• Resolution Flexibility: The model is able to process a range of image resolutions, and its computational requirements can be adjusted by setting minimum and maximum pixel counts to optimize performance for specific hardware. Images can be resized to a specific width and height.

Limitations

• The model has limitations in recognizing specific individuals or intellectual property.

• It may struggle with complex, multi-step instructions.

• Counting accuracy is not high in complex scenes.

• Spatial reasoning skills, especially in 3D spaces, require further improvements.