InternVL2_5-4B
| Property | Value |
|---|---|
| Model Size | 4B parameters |
| Architecture | ViT-MLP-LLM with InternViT-300M + Qwen2.5-3B |
| License | MIT License |
| Paper | arXiv:2412.05271 |
| Developer | OpenGVLab |
What is InternVL2_5-4B?
InternVL2_5-4B is a state-of-the-art multimodal large language model that combines a powerful vision encoder (InternViT-300M) with Qwen2.5-3B-Instruct language model. It represents a significant advancement in multimodal AI, capable of processing and understanding images, videos, and text while maintaining strong reasoning capabilities.
Implementation Details
The model follows a "ViT-MLP-LLM" paradigm, utilizing a randomly initialized MLP projector to bridge the vision and language components. It implements advanced features like dynamic resolution training and pixel unshuffle operations to optimize visual processing.
- Employs dynamic high-resolution training for multi-image and video processing
- Uses innovative data filtering pipeline to ensure high-quality training
- Implements random JPEG compression for enhanced robustness
- Features loss reweighting strategy for balanced training
Core Capabilities
- Multi-image processing with dynamic resolution handling
- Video understanding with frame-by-frame analysis
- Strong OCR and document understanding abilities
- Advanced reasoning and mathematics capabilities
- Multilingual understanding and visual grounding
- Streaming output support for real-time applications
Frequently Asked Questions
Q: What makes this model unique?
InternVL2_5-4B stands out for its efficient training strategy that requires only 120 billion tokens (compared to competitors' 1.4 trillion) while maintaining high performance. It also features an innovative progressive scaling strategy and advanced data filtering pipeline.
Q: What are the recommended use cases?
The model excels in various applications including multi-image analysis, video understanding, document processing, mathematical reasoning, and multilingual tasks. It's particularly suitable for applications requiring strong visual-language understanding and reasoning capabilities.
