R1-Onevision is a multimodal reasoning large model focused on complex visual reasoning tasks. It can integrate visual and textual information, accurately interpret multimodal content, and has strong capabilities in scenarios such as mathematics, science, deep image understanding, and logical reasoning, and can serve as an AI assistant for cross-domain problem solving.
Main Capabilities
- Cross-modal reasoning: by converting images into formal textual representations, it enables precise language-based reasoning.
- Multi-domain coverage: the dataset covers natural scenes, science, mathematics problems, OCR content, and complex charts, among others.
- Model fine-tuning: fine-tuned based on Qwen2.5-VL on the R1-Onevision dataset, enhancing visual-language understanding and reasoning capabilities.
- Accompanying resources: provides the dataset, benchmark (R1-Onevision-Bench), SFT weights, RL weights, and demo.
Usage
You can use the Hugging Face transformers library to load the model. The project provides links to the dataset, benchmark, SFT/RL weights, and Demo (see the repository page for specific links).
Applicable Scenarios
- Visual reasoning tasks
- Image understanding
- Mathematics and science problem solving
- Logical reasoning
- Complex chart interpretation
Limitations
- Specific functional details, charging information, pricing, platform support, usage results, etc. are not fully provided in the materials.
- Model performance and experimental details need to be referenced in the paper.
- Institution: Zhejiang University.
Related Links
- GitHub repository:https://github.com/Fancy-MLLM/R1-Onevision
- Dataset, benchmark, weights, and paper links are available on the repository page.
