InternVL2-Pretrain-Models
| Property | Value |
|---|---|
| License | MIT |
| Papers | InternVL Paper, InternVL 1.5 Report |
| Context Window | 8k tokens |
| Model Series | 1B to 108B parameters |
What is InternVL2-Pretrain-Models?
InternVL2 represents a significant advancement in multimodal large language models, offering a comprehensive suite of pre-trained models from the Stage-1 pre-training phase. These models are designed to establish strong foundational capabilities in processing and aligning multiple modalities including text, images, and videos.
Implementation Details
The architecture combines vision and language components, utilizing different backbone models based on size variant. For instance, the 1B variant uses InternViT-300M-448px for vision and Qwen2-0.5B-Instruct for language processing, while larger variants like the 76B model employ more sophisticated components such as InternViT-6B-448px-V1-5 and Hermes-2-Theta-Llama-3-70B.
- Supports multiple input modalities including text, images, and videos
- 8k context window for handling long-form content
- Available in various sizes (1B, 2B, 4B, 8B, 26B, 40B, and 76B parameters)
- Built on established vision and language architectures
Core Capabilities
- Document and chart comprehension
- Infographics QA processing
- Scene text understanding and OCR tasks
- Scientific and mathematical problem solving
- Cultural understanding and integrated multimodal processing
- Long-form content analysis
Frequently Asked Questions
Q: What makes this model unique?
InternVL2 stands out for its comprehensive multimodal capabilities and scalable architecture, offering competitive performance comparable to proprietary commercial models while remaining open-source. Its 8k context window and ability to process multiple images and videos in a single context make it particularly versatile.
Q: What are the recommended use cases?
The model excels in scenarios requiring deep understanding of mixed media content, including document analysis, technical documentation processing, educational content creation, and complex visual-linguistic tasks. It's particularly suitable for applications requiring both visual and textual understanding in areas like education, research, and content analysis.
