InternVL2-26B

InternVL2-26B

OpenGVLab

A powerful 25.5B parameter multimodal model combining InternViT-6B vision encoder and InternLM2-20B language model for advanced image/video understanding and generation.

PropertyValue
Parameter Count25.5B
LicenseMIT
PaperInternVL Paper
ArchitectureInternViT-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.

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