H2OVL-Mississippi-2B
| Property | Value |
|---|---|
| Parameter Count | 2 Billion |
| Model Type | Vision-Language Model |
| Training Data | 17M image-text pairs |
| Model Hub | Hugging Face |
What is h2ovl-mississippi-2b?
H2OVL-Mississippi-2B is a cutting-edge vision-language model developed by H2O.ai that bridges the gap between visual and textual understanding. Built on the foundation of H2O-Danube language models, this 2-billion parameter model represents a strategic balance between computational efficiency and performance, making it particularly suitable for real-world applications in document AI, OCR, and multimodal reasoning.
Implementation Details
The model architecture is optimized for vision-language tasks and demonstrates competitive performance against similar-sized models across various benchmarks. It achieves an impressive average score of 54.4 on the OpenVLM Leaderboard, outperforming several comparable models in specific tasks like Math Vista and MMStar evaluation metrics.
- Efficient Architecture: 2B parameters optimized for vision-language tasks
- Comprehensive Training: Trained on 17M image-text pairs for robust generalization
- Multiple Task Support: Excels in image captioning, VQA, and document understanding
- Integration Ready: Supports both Transformers and vLLM inference methods
Core Capabilities
- Document AI and OCR Processing
- Visual Question Answering (VQA)
- Image Captioning and Description
- Multi-image Understanding
- JSON Data Extraction from Visual Inputs
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its optimal balance between model size and performance, achieving competitive results against larger models while maintaining practical deployment capabilities. Its strong performance in document AI and OCR tasks makes it particularly valuable for business applications.
Q: What are the recommended use cases?
The model excels in document processing, image understanding, and multimodal reasoning tasks. It's particularly well-suited for applications requiring detailed image description, document analysis, and structured data extraction from visual inputs. The model also supports multi-image comparison and analysis.
