In ‘DeepSeek-VL: Towards Real-World Vision-Language Understanding’, DeepSeek-VL, a Vision-Language (VL) Model, is presented for practical applications through data diversity, a use-case taxonomy, and a hybrid vision encoder.

• The model focuses on real-world encompassing elements like web screenshots, OCR, and charts.
• A use-case specific dataset improves model’s practical application performance.
• The hybrid vision encoder balances efficiency and detail retention for high-res images.
• Integration of LLM capability preservation during pretraining maintains strong language abilities.
• Both 1.3B and 7B model sizes demonstrate outstanding performance in visual-language benchmarks and maintain robustness in language-centric benchmarks.
• DeepSeek-VL evidences the importance of practical use-case adaptation and efficient design in VL models, a stride forward for real-world AI applications.

This research emphasizes the significance of contextual relevance and efficiency in the succeeding generations of VL models, offering a foundation for future innovations. Read more.