InternVL2-8B-AWQ
| Property | Value |
|---|---|
| Model Size | 8B parameters |
| License | MIT License |
| Quantization | INT4 Weight-only (AWQ) |
| Paper | arXiv:2412.05271 |
What is InternVL2-8B-AWQ?
InternVL2-8B-AWQ is a state-of-the-art multimodal model that has been optimized using INT4 weight-only quantization through the AWQ algorithm. This model represents a significant advancement in efficient vision-language processing, achieving up to 2.4x faster inference speeds compared to FP16 implementations while maintaining high performance.
Implementation Details
The model leverages LMDeploy for deployment and supports various NVIDIA GPU architectures including Turing, Ampere, and Ada Lovelace. The implementation focuses on efficient inference through weight quantization while maintaining model quality.
- Supports batch inference and RESTful API service deployment
- Compatible with OpenAI-style interfaces
- Optimized for modern NVIDIA GPUs (20/30/40 series)
- Implements efficient weight-only quantization (W4A16)
Core Capabilities
- High-performance vision-language processing
- Efficient inference with reduced memory footprint
- Batch processing support
- REST API integration capabilities
- Compatibility with popular GPU architectures
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its efficient implementation of INT4 quantization while maintaining high performance levels, making it particularly suitable for production deployments where speed and resource efficiency are crucial.
Q: What are the recommended use cases?
The model is ideal for vision-language tasks requiring efficient processing, such as image description, visual question answering, and multimodal analysis in production environments where computational resources need to be optimized.
