InternVL2-26B
| Property | Value |
|---|---|
| Parameter Count | 25.5B |
| License | MIT |
| Paper | InternVL Paper |
| Architecture | InternViT-6B + InternLM2-20B |
What is InternVL2-26B?
InternVL2-26B is a state-of-the-art multimodal large language model that combines InternViT-6B vision encoder with InternLM2-20B language model. It's designed to handle complex visual-linguistic tasks with an 8k context window, supporting multiple images, long texts, and video inputs.
Implementation Details
The model architecture consists of three main components: InternViT-6B-448px-V1-5 for vision processing, an MLP projector for feature alignment, and internlm2-chat-20b for language understanding and generation. It uses BF16 precision and supports various deployment options including 8-bit quantization.
- 8k context window for handling long sequences
- Multi-image and video processing capabilities
- Support for streaming output generation
- Flexible deployment options across multiple GPUs
Core Capabilities
- Document and chart comprehension (92.9% on DocVQA)
- Scene text understanding and OCR tasks
- Video analysis and description
- Cultural understanding and scientific problem solving
- Multi-turn conversations about visual content
- Grounding capabilities with 88.5% average accuracy
Frequently Asked Questions
Q: What makes this model unique?
InternVL2-26B stands out for its comprehensive multimodal capabilities, competitive performance against commercial models, and ability to handle multiple images and videos in a single conversation. It achieves state-of-the-art results across various benchmarks while maintaining open-source accessibility.
Q: What are the recommended use cases?
The model excels in document analysis, chart interpretation, video understanding, scientific problem solving, and general visual-linguistic tasks. It's particularly suitable for applications requiring sophisticated understanding of mixed visual and textual content, such as automated document processing, educational tools, and content analysis systems.
