| Property | Value |
|---|---|
| Model Type | Multimodal Large Language Model |
| Vision Encoder | InternViT-6B-448px-V2_5 |
| Language Model | Qwen2.5-72B-Instruct |
| License | MIT License (with Qwen License components) |
| Paper | arXiv:2412.05271 |
What is InternVL2_5-78B?
InternVL2_5-78B is a state-of-the-art multimodal large language model that combines a powerful InternViT-6B vision encoder with the Qwen2.5-72B-Instruct language model. It represents the largest model in the InternVL 2.5 series, designed to handle complex visual-language tasks with exceptional performance. The model employs a "ViT-MLP-LLM" architecture paradigm with advanced training strategies and high-quality data filtering.
Implementation Details
The model implements a sophisticated three-stage training pipeline: MLP Warmup for cross-modal alignment, optional ViT Incremental Learning for domain adaptation, and Full Model Instruction Tuning. It uses dynamic high-resolution processing for images, supporting both single and multi-image inputs, as well as video understanding.
- Progressive scaling strategy requiring only 120B tokens compared to competitors' 1.4T tokens
- Advanced data filtering pipeline to ensure high-quality training data
- Random JPEG compression for enhanced robustness
- Dynamic resolution strategy with support for multi-image and video processing
Core Capabilities
- Advanced visual-language understanding and reasoning
- Multi-image and video comprehension
- OCR and document understanding
- Mathematical reasoning with visual inputs
- Multimodal multilingual understanding
- Reduced hallucination through strict data quality controls
Frequently Asked Questions
Q: What makes this model unique?
InternVL2_5-78B stands out for its efficient training approach using progressive scaling and high-quality data filtering, achieving state-of-the-art performance while using significantly fewer training tokens than competitors. It also maintains strong pure language capabilities while excelling at visual tasks.
Q: What are the recommended use cases?
The model excels in complex visual-language tasks including detailed image description, multi-image comparison, video understanding, document analysis, and mathematical reasoning with visual inputs. It's particularly suitable for applications requiring sophisticated multimodal understanding and reasoning.