InternVL2-Pretrain-Models
| Property | Value |
|---|---|
| License | MIT |
| Pipeline Tag | Image-Text-to-Text |
| Papers | InternVL Paper, Technical Report |
| Context Window | 8k tokens |
What is InternVL2-Pretrain-Models?
InternVL2 represents a significant advancement in multimodal AI, offering a series of models ranging from 1 billion to 108 billion parameters. These models are designed for Stage-1 pre-training, focusing on aligning multimodal inputs including text, images, and videos. The architecture demonstrates competitive performance against both open-source and commercial models in various applications.
Implementation Details
The model utilizes a sophisticated architecture combining vision and language components. It features different variants, from the compact 1B model using InternViT-300M-448px for vision and Qwen2-0.5B-Instruct for language, up to the massive 76B version leveraging advanced architectures like Llama-3.
- Multimodal integration with 8k context window support
- Comprehensive training on long texts, multiple images, and videos
- Advanced vision-language alignment capabilities
Core Capabilities
- Document and chart comprehension
- Infographics QA and scene text understanding
- OCR tasks and scientific problem solving
- Cultural understanding and integrated multimodal processing
- Long-form content analysis and generation
Frequently Asked Questions
Q: What makes this model unique?
InternVL2 stands out for its comprehensive multimodal capabilities and scalable architecture, offering versions from 1B to 108B parameters while maintaining high performance across various tasks. The 8k context window and advanced training on diverse data types make it particularly versatile.
Q: What are the recommended use cases?
The model excels in document analysis, scientific problem-solving, visual-linguistic tasks, and cultural understanding. It's particularly suitable for applications requiring deep integration of text, image, and video understanding.
