InternVL2_5-8B

InternVL2_5-8B

OpenGVLab

InternVL2_5-8B is an 8B parameter multimodal LLM combining InternViT vision encoder and InternLM2.5-7B chat model, offering advanced visual-language capabilities with efficient training strategy.

PropertyValue
Vision EncoderInternViT-300M-448px-V2_5
Language Modelinternlm2_5-7b-chat
LicenseMIT License
PaperarXiv:2412.05271

What is InternVL2_5-8B?

InternVL2_5-8B is a sophisticated multimodal large language model that combines a powerful vision encoder (InternViT) with an advanced language model (InternLM2.5-7B). It represents a significant advancement in the InternVL series, featuring enhanced training strategies and improved data quality for better visual-language understanding.

Implementation Details

The model follows a "ViT-MLP-LLM" architecture paradigm, utilizing a randomly initialized MLP projector to connect the vision and language components. It implements dynamic resolution handling for images up to 448×448 pixels and supports both single and multi-image processing, as well as video understanding.

  • Progressive scaling strategy using only 120 billion tokens during training
  • Dynamic high-resolution training for multiple image and video inputs
  • Advanced data filtering pipeline for maintaining high-quality training data
  • Random JPEG compression for improved robustness

Core Capabilities

  • Multi-image and video understanding
  • High-resolution image processing
  • Multimodal reasoning and mathematics
  • OCR and chart comprehension
  • Visual grounding and multilingual understanding

Frequently Asked Questions

Q: What makes this model unique?

The model's unique strength lies in its efficient training strategy and comprehensive multimodal capabilities, achieving strong performance while using significantly fewer training tokens than competitors. It also features an innovative progressive scaling approach that enables efficient transfer learning across different model sizes.

Q: What are the recommended use cases?

The model excels in various applications including visual question answering, image and video description, document understanding, mathematical reasoning with visual inputs, and multilingual visual tasks. It's particularly effective for scenarios requiring high-resolution image processing and multi-image analysis.

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