Ovis2-1B
| Property | Value |
|---|---|
| Model Type | Multimodal Large Language Model |
| Base Architecture | aimv2-large-patch14-448 (Vision) + Qwen2.5-0.5B-Instruct (Language) |
| License | Apache License 2.0 |
| Paper | arXiv:2405.20797 |
What is Ovis2-1B?
Ovis2-1B is a cutting-edge multimodal large language model that represents a significant advancement in visual-language AI technology. As part of the Ovis2 series, it employs innovative architectural design focused on structural alignment between visual and textual embeddings, making it particularly efficient at processing and understanding both image and text data.
Implementation Details
The model combines an aimv2-large-patch14-448 vision transformer with Qwen2.5-0.5B-Instruct language model, optimized for efficient multimodal processing. It supports various input formats including single images, multiple images, and video content, with a context window of up to 32768 tokens.
- Advanced visual-text embedding alignment architecture
- Optimized small-model performance with high capability density
- Comprehensive multilingual OCR support
- Integrated video and multi-image processing capabilities
Core Capabilities
- Strong performance in OCR tasks (89.0% on OCRBench)
- Enhanced Chain-of-Thought reasoning through instruction tuning
- Video analysis with support for multiple frames
- Complex visual information processing across multiple images
- Structured data extraction from tables and charts
- Competitive performance across various benchmarks including MMBench and MMStar
Frequently Asked Questions
Q: What makes this model unique?
Ovis2-1B stands out for its efficient performance despite its relatively small size, particularly in OCR tasks where it outperforms larger models. Its structural embedding alignment approach enables better visual-language understanding while maintaining computational efficiency.
Q: What are the recommended use cases?
The model is well-suited for applications requiring visual-text understanding, including document analysis, image description, visual question answering, and video content analysis. It's particularly effective for scenarios requiring OCR capabilities or multi-image processing.
