Ovis2-16B
| Property | Value |
|---|---|
| Model Type | Multimodal Large Language Model |
| Base Architecture | Qwen2.5-14B-Instruct + aimv2-huge-patch14-448 |
| License | Apache License 2.0 |
| Paper | arXiv:2405.20797 |
What is Ovis2-16B?
Ovis2-16B is an advanced multimodal large language model that represents a significant evolution in the Ovis series. It's designed to create structural alignment between visual and textual embeddings, combining a Qwen2.5-14B-Instruct language model with the aimv2-huge-patch14-448 vision architecture. The model demonstrates remarkable performance across various benchmarks, particularly in visual reasoning and multilingual capabilities.
Implementation Details
The model integrates sophisticated architectural components that enable efficient processing of both visual and textual inputs. It supports multiple input modalities including single images, multiple images, and video content, with a context window of up to 32,768 tokens.
- Implements advanced visual processing through the aimv2-huge-patch14-448 architecture
- Utilizes Qwen2.5-14B-Instruct as the base language model
- Supports batch processing for efficient inference
- Features comprehensive multimodal preprocessing capabilities
Core Capabilities
- Strong performance in visual reasoning tasks with scores of 85.6 on MMBench-V1.1test
- Advanced OCR capabilities with 87.9% accuracy on OCRBench
- Video processing with strong performance on VideoMME (70.0/74.4 without/with subtitles)
- Multilingual support and structured data extraction
- Chain-of-Thought reasoning enhanced through instruction tuning
Frequently Asked Questions
Q: What makes this model unique?
Ovis2-16B stands out for its structural embedding alignment approach and exceptional performance despite its relatively smaller size compared to larger models like Qwen-VL-72B. It achieves competitive results across various benchmarks while maintaining efficient processing capabilities.
Q: What are the recommended use cases?
The model excels in various applications including visual question answering, image and video analysis, OCR tasks, and complex reasoning scenarios. It's particularly well-suited for applications requiring multilingual capabilities and structured data extraction from visual content.
