InternVL2_5-Pretrain-Models
| Property | Value |
|---|---|
| License | MIT |
| Author | OpenGVLab |
| Model Type | Multimodal LLM |
| Paper | arXiv:2412.05271 |
What is InternVL2_5-Pretrain-Models?
InternVL 2.5 represents a significant advancement in multimodal large language models, building upon the foundation of InternVL 2.0. This series offers a comprehensive range of models from 1B to 78B parameters, combining sophisticated vision processing with powerful language capabilities.
Implementation Details
The architecture integrates specialized vision components (InternViT) with various language models including Qwen2.5 and InternLM2.5. The series features models pre-trained in either Stage-1 or Stage-1.5, with larger models like InternVL2.5-8B and InternVL2.5-26B utilizing Stage-1.5 weights.
- Vision processing through InternViT models (300M and 6B variants)
- Language processing through Qwen2.5 and InternLM2.5 models
- Multiple model sizes for different use cases (1B to 78B parameters)
Core Capabilities
- Advanced multimodal understanding and generation
- Flexible model scaling options
- Enhanced training and testing strategies
- Improved data quality processing
Frequently Asked Questions
Q: What makes this model unique?
InternVL 2.5 stands out through its scalable architecture and improved training strategies, offering various model sizes while maintaining high performance across both vision and language tasks.
Q: What are the recommended use cases?
The model series is suitable for a wide range of multimodal applications, from lightweight deployment (1B model) to high-performance enterprise solutions (78B model), supporting tasks that require both visual and textual understanding.
