Insight-V-Reason-LLaMA3
| Property | Value |
|---|---|
| Parameter Count | 8.35B |
| Model Type | Visual Reasoning Language Model |
| Languages | English, Chinese |
| License | Apache 2.0 |
| Paper | arXiv:2411.14432 |
| Context Window | 32K tokens |
What is Insight-V-Reason-LLaMA3?
Insight-V-Reason-LLaMA3 is an advanced visual reasoning model built on the LLaMA3-8B architecture, enhanced with Oryx-ViT for visual processing capabilities. This model represents a significant advancement in multi-modal language modeling, specifically designed for complex visual reasoning tasks with a generous 32K token context window.
Implementation Details
The model combines a pre-trained Oryx-ViT visual processor with LLaMA3-8B language model, trained on 200,000 reasoning-focused datasets. It operates in BFloat16 precision and was developed using significant computational resources (64 NVIDIA Tesla A100 GPUs) with PyTorch and HuggingFace Trainer.
- Multi-agent system for decomposing visual reasoning tasks
- Two-stage training pipeline for enhanced reasoning capabilities
- Scalable data generation pipeline for high-quality reasoning data
Core Capabilities
- Long-chain visual reasoning
- Multi-language support (English and Chinese)
- High-context understanding (32K tokens)
- Task decomposition and summarization
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its multi-agent approach to visual reasoning, decomposing complex tasks into manageable components while maintaining high accuracy through its two-stage training pipeline.
Q: What are the recommended use cases?
This model is ideal for complex visual reasoning tasks, multi-language applications requiring visual understanding, and scenarios needing long-context comprehension with visual elements.
