DeepSeek-VL2: Mixture-of-Experts Vision-Language Models

What is the project about?

DeepSeek-VL2 is a series of advanced, large, Mixture-of-Experts (MoE) vision-language models. It's designed for multimodal understanding, meaning it can process and understand both images and text. It's an improvement over its predecessor, DeepSeek-VL.

What problem does it solve?

The project aims to provide powerful, open-source models that can perform complex tasks requiring understanding of both visual and textual information. It addresses the need for models that can perform well on tasks like:

• Visual Question Answering (VQA): Answering questions about images.
• Optical Character Recognition (OCR): Extracting text from images.
• Document/Table/Chart Understanding: Interpreting and extracting information from structured visual data.
• Visual Grounding: Locating objects within an image based on a textual description (and providing bounding box coordinates).

It solves these problems efficiently, achieving strong performance with a relatively small number of activated parameters, thanks to its MoE architecture.

What are the features of the project?

• Mixture-of-Experts (MoE) Architecture: This allows the model to be large overall but only activate a subset of its parameters for each input, improving efficiency.
• Three Model Variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small, and DeepSeek-VL2, offering different sizes and performance levels (1.0B, 2.8B, and 4.5B activated parameters, respectively).
• Multimodal Understanding: Processes both images and text.
• Visual Grounding Capability: Supports object localization with bounding box outputs using special tokens (<|ref|>, <|/ref|>, <|det|>, <|/det|>).
• Hugging Face Integration: Models are available on Hugging Face for easy download and use.
• Quick Start Examples: Provides code examples for simple inference with single and multiple images.
• Incremental Prefilling: Supports a memory-saving inference technique, crucial for running larger models on GPUs with limited memory.
• Gradio Demo: Includes a web demo for interactive use.
• VLMEvalKit Support: Compatible with a toolkit for evaluating vision-language models.
• Support for interleaved image-text input.

What are the technologies used in the project?

• Python: The primary programming language.
• PyTorch: The deep learning framework.
• Transformers (Hugging Face): Library for working with pre-trained models.
• Gradio: For creating the web demo.
• CUDA: For GPU acceleration.

What are the benefits of the project?

• State-of-the-Art Performance: Achieves competitive or state-of-the-art results compared to other open-source models.
• Efficiency: The MoE architecture allows for strong performance with fewer activated parameters, reducing computational cost.
• Open Source: The models and code are publicly available, promoting research and development.
• Commercial Use Supported: The license allows for commercial applications.
• Easy to Use: Integration with Hugging Face and provided examples make it relatively easy to get started.
• Flexibility: Different model sizes cater to various resource constraints.

What are the use cases of the project?

• Image Captioning: Generating descriptive text for images.
• Visual Question Answering Systems: Building interactive systems that answer questions about images.
• Document Processing Automation: Extracting information from scanned documents, forms, and charts.
• Robotics: Enabling robots to understand and interact with their visual environment.
• Accessibility Tools: Assisting visually impaired users by describing images and their content.
• Content Moderation: Identifying inappropriate or harmful content in images and videos.
• E-commerce: Improving product search and recommendation through visual understanding.
• Education: Creating interactive learning materials that combine visual and textual information.
• Research: A strong baseline for further research in vision-language modeling.