Insight-V-Reason
| Property | Value |
|---|---|
| Parameter Count | 8.06B |
| Model Type | Vision-Language Model |
| License | Apache 2.0 |
| Paper | Research Paper |
| Languages | English, Chinese |
| Base Model | Qwen2.5-7B-Instruct |
What is Insight-V-Reason?
Insight-V-Reason is an advanced vision-language model that combines Oryx-ViT with Qwen2.5-7B architecture to enable sophisticated visual reasoning capabilities. It features a 32K token context window and leverages a unique multi-agent system for decomposing visual reasoning tasks.
Implementation Details
The model is implemented using PyTorch and trained on 64 NVIDIA Tesla A100 GPUs. It processes data in BFloat16 precision and incorporates a dataset of 200k reasoning examples.
- Pre-trained using Oryx-ViT visual encoder
- Two-stage training pipeline for enhanced reasoning
- Scalable data generation pipeline for long-chain reasoning
Core Capabilities
- Long-context visual reasoning with 32K token window
- Multi-agent task decomposition
- Bilingual support (English and Chinese)
- High-quality reasoning data generation
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its multi-agent system that separates visual reasoning into discrete reasoning and summarization steps, combined with a novel two-stage training pipeline.
Q: What are the recommended use cases?
The model is particularly suited for complex visual reasoning tasks, long-chain reasoning scenarios, and bilingual applications requiring sophisticated visual understanding and analysis.
