Ovis2-34B
| Property | Value |
|---|---|
| Model Size | 34B parameters |
| Vision Encoder | aimv2-1B-patch14-448 |
| Language Model | Qwen2.5-32B-Instruct |
| License | Apache License 2.0 |
| Paper | arXiv:2405.20797 |
What is Ovis2-34B?
Ovis2-34B is an advanced multimodal large language model that represents a significant evolution in the Ovis series. It's designed to structurally align visual and textual embeddings, offering state-of-the-art performance across various multimodal tasks. The model leverages a 34B parameter architecture, combining aimv2-1B-patch14-448 for visual processing with Qwen2.5-32B-Instruct for language understanding.
Implementation Details
The model architecture implements a sophisticated approach to multimodal processing, supporting various input types including single images, multiple images, and video content. It features a maximum context length of 32,768 tokens and utilizes bfloat16 precision for efficient processing.
- Optimized visual-language alignment through structural embedding techniques
- Enhanced Chain-of-Thought reasoning capabilities through instruction tuning
- Comprehensive video and multi-image processing support
- Advanced multilingual OCR capabilities
Core Capabilities
- Strong performance on benchmark tests including MMBench-V1.1 (86.6%), MathVista (76.1%), and MMVet (77.1%)
- Advanced video processing with high scores on VideoMME (75.6% with subtitles)
- Sophisticated reasoning abilities across multiple domains
- Robust multilingual support and structured data extraction
Frequently Asked Questions
Q: What makes this model unique?
Ovis2-34B stands out for its structural embedding alignment approach and comprehensive multimodal capabilities, particularly in video processing and reasoning tasks. It achieves competitive performance against larger models while maintaining efficient processing capabilities.
Q: What are the recommended use cases?
The model excels in various applications including complex visual analysis, video understanding, multilingual OCR, structured data extraction from visual elements, and chain-of-thought reasoning tasks. It's particularly suitable for applications requiring sophisticated multimodal understanding.
