Ovis2-4B
| Property | Value |
|---|---|
| Base Architecture | Qwen2.5-3B-Instruct with aimv2-huge-patch14-448 Vision Encoder |
| License | Apache License 2.0 |
| Paper | arXiv:2405.20797 |
| Framework | PyTorch |
What is Ovis2-4B?
Ovis2-4B is an advanced multimodal large language model that represents a significant evolution in the Ovis series. It combines a 3B parameter language model (Qwen2.5) with a sophisticated vision encoder (aimv2-huge) to create a powerful 4B parameter system capable of processing both text and visual information. The model excels particularly in structural alignment between visual and textual embeddings, offering enhanced performance across various multimodal tasks.
Implementation Details
The model employs a hybrid architecture that integrates advanced visual processing capabilities with robust language understanding. It utilizes the aimv2-huge-patch14-448 vision encoder for image processing and the Qwen2.5-3B-Instruct foundation for language tasks, enabling seamless multimodal interactions.
- Optimized training strategies for high capability density in a relatively small model size
- Advanced Chain-of-Thought reasoning through instruction tuning and preference learning
- Comprehensive video and multi-image processing capabilities
- Enhanced multilingual OCR support and structured data extraction
Core Capabilities
- Strong performance on OCR tasks (91.1% on OCRBench)
- Competitive results on visual reasoning benchmarks (81.4% on MMBench-V1.1test)
- Video analysis capabilities with strong temporal understanding
- Multi-image processing with contextual understanding
- Structured data extraction from complex visual elements including tables and charts
Frequently Asked Questions
Q: What makes this model unique?
Ovis2-4B stands out for its structural embedding alignment approach, which enables better integration of visual and textual information. Despite its relatively compact size, it achieves competitive performance against larger models, particularly in OCR and visual reasoning tasks.
Q: What are the recommended use cases?
The model is well-suited for applications requiring complex visual-language understanding, including document analysis, video content description, multi-image comparison, and tasks requiring detailed reasoning about visual content. It's particularly effective for applications needing strong OCR capabilities and structured data extraction.
