Qwen2-VL - A state-of-the-art vision-language multimodal model for complex document and video understanding.

## Introduction to Qwen2-VL Qwen2-VL is a vision-language multimodal model developed by the Qwen team at Alibaba Cloud. It is designed to handle complex PDF documents and video content, excelling in image and video understanding, document parsing, and object localization. The model supports multiple languages and resolutions, making it versatile for various applications. ## Key Features of Qwen2-VL The key features of Qwen2-VL include: - **Image Understanding**: Supports various resolutions and proportions using Naive Dynamic Resolution and Multimodal Rotary Position Embedding (M-ROPE) techniques. - **Video Understanding**: Capable of understanding videos longer than 20 minutes, suitable for high-quality video Q&A, dialogue, and content creation. - **Agent Functionality**: Can be integrated into devices like smartphones and robots, supporting complex reasoning and decision-making. - **Multilingual Support**: Supports English, Chinese, European languages, Japanese, Korean, Arabic, and Vietnamese, catering to a global user base. ## Key Features of Qwen2-VL The main functions of Qwen2-VL include: - **Image and Video Understanding**: Processes single images, multiple images, and long videos, supporting dynamic resolution inputs. - **Document Parsing**: Excels in handling complex PDF layouts, extracting content such as tables and headings, and supporting multi-scene, multilingual documents. - **Object Localization**: Provides precise object detection, pointing, and counting, with support for absolute coordinates and JSON format output. ## Access and Usage of Qwen2-VL Qwen2-VL can be accessed and used in the following ways: - **Open Source Models**: The 2B and 7B versions are available on Hugging Face and ModelScope under the Apache 2.0 license. - **API Access**: The 72B version is accessible via DashScope API, requiring registration. - **Development Tools**: Supports Hugging Face Transformers, vLLM, AutoGPTQ, AutoAWQ, and Llama-Factory for quantization, deployment, and fine-tuning. The qwen-vl-utils toolkit can be installed via pip for base64, URL, and interleaved image/video input. - **Usage Examples**: Official code snippets are provided, with recommendations to use flash attention 2 for acceleration. These can be found on the GitHub repository and the official blog. ## Limitations of Qwen2-VL The limitations of Qwen2-VL include: - **Audio Extraction**: Cannot extract audio from videos. - **Knowledge Cutoff**: Knowledge is current up to June 2023. - **Weaknesses**: Struggles with counting, character recognition, and 3D spatial perception. - **Complex Scenarios**: May not guarantee accuracy in complex scenarios. ## Future Plans for Qwen2-VL The Qwen team plans to build more powerful vision-language models and integrate additional modalities, moving towards the development of an omni model. This could open up more application scenarios and enhance the model's capabilities. ## Resources for Qwen2-VL More information about Qwen2-VL can be found at the following resources: - **Official Blog**: [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://qwenlm.github.io/blog/qwen2-vl/) - **Hugging Face Model**: [Qwen/Qwen2-VL-7B-Instruct Model Details](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) - **ModelScope Organization Page**: [ModelScope Organization](https://modelscope.cn/organization/qwen) - **Community Support**: [Discord Community](https://discord.gg/yPEP2vHTu4) ### Citation sources: - [Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl) - Official URL Updated: 2025-03-28