InternVL2-8B
| Property | Value |
|---|---|
| Parameter Count | 8.08B parameters |
| Model Type | Multimodal LLM |
| License | MIT |
| Paper | InternVL Paper |
| Architecture | InternViT-300M-448px + MLP Projector + InternLM2-7B |
What is InternVL2-8B?
InternVL2-8B is part of the InternVL 2.0 series, representing a significant advancement in multimodal large language models. It combines a powerful vision encoder (InternViT-300M-448px) with a sophisticated language model (InternLM2-7B) through an MLP projector, creating a versatile system capable of understanding and processing both visual and textual information.
Implementation Details
The model features an 8k context window and utilizes BF16 precision for optimal performance. It's designed with a sophisticated architecture that enables processing of multiple images, long text sequences, and even video content. The implementation supports various deployment options, including 4-bit and 8-bit quantization for resource-efficient inference.
- Supports multiple GPU deployment with automatic device mapping
- Includes streaming output capabilities for real-time response generation
- Features built-in support for video frame processing and multi-image analysis
- Offers flexible deployment options through LMDeploy integration
Core Capabilities
- Document and Chart Understanding (91.6% on DocVQA test)
- Scene Text Recognition and OCR Tasks
- Multi-image Analysis and Comparison
- Video Understanding with frame-by-frame processing
- Cultural and Scientific Problem Solving
- Visual Grounding with 82.9% average accuracy
Frequently Asked Questions
Q: What makes this model unique?
InternVL2-8B stands out for its exceptional balance between model size and performance, achieving competitive results against larger models while maintaining efficient resource usage. It particularly excels in document understanding and OCR tasks, surpassing many open-source alternatives.
Q: What are the recommended use cases?
The model is ideal for applications requiring document analysis, chart interpretation, scientific problem-solving, and complex visual-linguistic tasks. It's particularly well-suited for scenarios requiring understanding of multiple images or video content within a single context.
