DeepSeek-VL2
by DeepSeekDeepSeek-VL2 is an open-source series of large-scale Mixture-of-Experts (MoE) vision-language models developed by DeepSeek. It significantly improves upon its predecessor, DeepSeek-VL, and excels in tasks such as visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. The model series includes three versions: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small, and DeepSeek-VL2, with 1.0B, 2.8B, and 4.5B activated parameters, respectively. DeepSeek-VL2 supports resolutions up to 1152x1152 and extreme aspect ratios of 1:9 or 9:1, making it versatile for various applications. It also features advanced capabilities like understanding scientific charts and generating Python code from images using the Plot2Code feature.
What is DeepSeek-VL2?
DeepSeek-VL2 is an open-source series of large-scale Mixture-of-Experts (MoE) vision-language models developed by DeepSeek. It significantly improves upon its predecessor, DeepSeek-VL, and excels in tasks such as visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. The model series includes three versions: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small, and DeepSeek-VL2, with 1.0B, 2.8B, and 4.5B activated parameters, respectively.
Main Features of DeepSeek-VL2
- Dynamic Resolution Support: Handles images with resolutions up to 1152x1152, supporting extreme aspect ratios of 1:9 or 9:1.
- Chart Understanding: Understands various scientific charts by learning from research document data.
- Plot2Code: Generates Python code from images.
- Meme Recognition: Parses and understands various memes.
- Visual Grounding: Performs zero-shot visual grounding, finding objects in images based on natural language descriptions.
- Visual Storytelling: Connects multiple images to form a visual story.
Technical Principles of DeepSeek-VL2
- Multi-Head Latent Attention (MLA): Uses low-rank key-value joint compression to eliminate bottlenecks in key-value caching during inference.
- DeepSeekMoE Architecture: Adopts a high-performance MoE architecture in feed-forward networks, reducing training costs.
- Cost-Effective Training and Inference: Leverages a diverse corpus of 8.1 trillion tokens, saving 42.5% in training costs compared to DeepSeek 67B.
- Long Context Windows: Supports context windows up to 128K in length.
Application Scenarios of DeepSeek-VL2
- Chatbots: Enables natural language interaction with users.
- Image Captioning: Generates descriptive text based on image content.
- Code Generation: Generates code based on user requirements, applicable in programming and software development fields.
Project Address of DeepSeek-VL2
- Github Repository: https://github.com/deepseek-ai/DeepSeek-VL2
- HuggingFace Model Library: https://huggingface.co/deepseek-ai/deepseek-vl2
Model Capabilities
Model Type
multimodal
Supported Tasks
Visual Question Answering
Optical Character Recognition
Document Understanding
Table Understanding
Chart Understanding
Visual Grounding
Code Generation
Meme Recognition
Visual Storytelling
Tags
Vision-Language Model
Mixture-of-Experts
AI
Open Source
Computer Vision
Natural Language Processing
Multimodal AI
Document Understanding
Code Generation
Visual Grounding
Usage & Integration
Pricing
free
License
Open Source
Screenshots & Images
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