What is QVQ-72B-Preview
QVQ-72B-Preview is an open-source multimodal reasoning model from the Alibaba Cloud Tongyi Qianwen team, focused on enhancing visual reasoning capabilities. The model performs excellently across multiple benchmarks and demonstrates strong capabilities in multimodal understanding and reasoning tasks. It can accurately understand image content, perform complex step-by-step reasoning, support inferring specific information such as the height and quantity of objects from images, and identify the deeper meaning of images, such as the connotations of memes.
Main Features of QVQ-72B-Preview
- Powerful Visual Reasoning Capabilities: QVQ-72B-Preview can accurately understand image content and perform complex step-by-step reasoning. It supports inferring specific information such as the height and quantity of objects from images, and can identify the deeper meaning of images, such as the connotations of memes.
- Multimodal Processing: The model can simultaneously process image and text information and perform deep reasoning. It can seamlessly connect language information with visual information, making AI reasoning processes more efficient.
- Scientific-Grade Reasoning Performance: QVQ-72B-Preview performs excellently when handling complex scientific problems and can think like a scientist and provide accurate answers. By questioning assumptions and optimizing reasoning steps, it provides more reliable and smarter results.
Performance Evaluation of QVQ-72B-Preview
QVQ-72B-Preview was evaluated on the following four datasets:
- MMMU: A university-level multidisciplinary multimodal evaluation dataset that assesses the model's vision-related comprehensive understanding and reasoning abilities, with a visual reasoning score of 70.3, reaching university level.
- MathVista: A mathematics-centered visual reasoning test set used to evaluate functions such as logical reasoning using puzzle test figures, algebraic reasoning using function graphs, and scientific reasoning using figures from academic papers. It surpasses OpenAI o1 and demonstrates powerful mathematical and graphical reasoning capabilities.
- MathVision: A high-quality multimodal mathematical reasoning test set derived from real mathematics competitions. Compared with MathVista, it has greater question diversity and disciplinary breadth, and its performance surpasses GPT-4o and Claude 3.5.
- OlympiadBench: An Olympiad-level bilingual multimodal science benchmark test set containing 8,476 questions from Olympiad mathematics and physics competitions (including China's Gaokao), with performance surpassing GPT-4o and Claude 3.5..
Project Address of QVQ-72B-Preview
- Project Official Website：Qwen Chat
- HuggingFace Model Repository：https://huggingface.co/Qwen/QVQ-72B-Preview
Application Scenarios of QVQ-72B-Preview
- Education: In knowledge transmission and learning scenarios, QVQ-72B-Preview can help teachers and students solve difficult problems such as deriving complex mathematical formulas and analyzing the principles of scientific experiments.
- Scientific Research Exploration: In the face of scientific research problems requiring deep investigation, such as interpreting quantum mechanical phenomena in physics or constructing galaxy evolution models in astronomy, QVQ-72B-Preview can assist scientists in uncovering the truths hidden behind data and phenomena.
- Multimodal Interaction: Whether in intelligent customer service responding to users' inquiries with both images and text, or in social media platforms accurately classifying and managing massive amounts of image-text information, QVQ-72B-Preview can perfectly integrate image and text information and provide ideal responses that meet user needs.