Insight-V-Summary
| Property | Value |
|---|---|
| Parameter Count | 8.06B |
| Model Type | Visual Language Model |
| Architecture | Qwen2.5-7B-Instruct + Oryx-ViT |
| License | Apache 2.0 |
| Research Paper | arXiv:2411.14432 |
What is Insight-V-Summary?
Insight-V-Summary is an advanced visual language model that combines the power of Qwen2.5 language model with Oryx-ViT visual processing capabilities. Built with a substantial context window of 32K tokens, this model represents a significant advancement in visual reasoning and summarization tasks.
Implementation Details
The model is implemented using PyTorch and was trained on 64 NVIDIA Tesla A100 GPUs. It processes data in BFloat16 precision and leverages a dataset of 1.2M image-text pairs. The architecture integrates pre-trained Oryx-ViT for visual processing with the Qwen2.5-7B language model.
- Multi-agent system for task decomposition
- Scalable data generation pipeline
- Two-stage training approach
- Bilingual support (English and Chinese)
Core Capabilities
- Long-chain reasoning with 32K token context window
- Visual reasoning and summarization
- Multi-agent task decomposition
- High-quality reasoning data generation
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its multi-agent system that effectively decomposes visual reasoning tasks into separate reasoning and summarization components, combined with its innovative two-stage training pipeline.
Q: What are the recommended use cases?
This model is particularly well-suited for complex visual reasoning tasks, long-form content summarization, and bilingual applications requiring sophisticated visual understanding and explanation capabilities.
