Insight-V-Summary-LLaMA3
| Property | Value |
|---|---|
| Parameter Count | 8.35B |
| Base Model | LLaMA3-8B |
| License | Apache-2.0 |
| Languages | English, Chinese |
| Paper | Research Paper |
What is Insight-V-Summary-LLaMA3?
Insight-V-Summary-LLaMA3 is an advanced visual reasoning model that combines the power of LLaMA3-8B with Oryx-ViT for enhanced image processing capabilities. It features a remarkable 32K token context window and is specifically designed for complex visual reasoning tasks through a multi-agent system approach.
Implementation Details
The model is built on a sophisticated architecture that combines pre-trained Oryx-ViT with LLaMA3-8B, trained on 1.2M image-text pairs. It utilizes BFloat16 precision and was developed using 64 NVIDIA Tesla A100 GPUs, implemented in PyTorch using the HuggingFace Trainer.
- Scalable data generation pipeline for long-chain reasoning
- Multi-agent system for task decomposition
- Two-stage training pipeline for enhanced visual reasoning
- 32K token context window support
Core Capabilities
- Visual reasoning and analysis
- Bilingual support (English and Chinese)
- Long-context processing
- High-quality reasoning chain generation
- Task decomposition and summarization
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 extensive 32K token context window and bilingual capabilities.
Q: What are the recommended use cases?
This model is particularly well-suited for complex visual reasoning tasks, long-form visual analysis, and applications requiring detailed image understanding in both English and Chinese contexts.
