DeepSeek-VL2-Small
| Property | Value |
|---|---|
| Parameter Count | 2.8B activated parameters |
| Model Type | Mixture-of-Experts Vision-Language Model |
| License | MIT License (Code), DeepSeek Model License (Model) |
| Paper | arXiv:2412.10302 |
What is deepseek-vl2-small?
DeepSeek-VL2-Small is part of the advanced DeepSeek-VL2 series, representing a significant evolution in vision-language models. Built on DeepSeekMoE-16B architecture, this model variant contains 2.8B activated parameters, positioning itself as a balanced option between the Tiny (1.0B) and full (4.5B) versions.
Implementation Details
The model leverages a sophisticated Mixture-of-Experts (MoE) architecture, implementing dynamic tiling strategies for image processing. It's optimized to handle multiple images efficiently, with special handling for scenarios involving 3 or more images through 384x384 padding.
- Built on DeepSeekMoE-16B architecture
- Supports bfloat16 precision for efficient inference
- Implements dynamic tiling for optimal image processing
- Recommended temperature setting of T ≤ 0.7 for best generation quality
Core Capabilities
- Visual Question Answering (VQA)
- Optical Character Recognition (OCR)
- Document and Table Understanding
- Chart Analysis
- Visual Grounding
- Multi-image Processing
Frequently Asked Questions
Q: What makes this model unique?
The model's MoE architecture allows it to achieve competitive or state-of-the-art performance with fewer activated parameters compared to traditional dense models. Its ability to handle multiple images and various visual understanding tasks makes it versatile for real-world applications.
Q: What are the recommended use cases?
The model excels in scenarios requiring sophisticated visual understanding, including document analysis, visual QA, and complex image-text interactions. It's particularly suitable for commercial applications, thanks to its permissive licensing terms.
