DeepSeek-VL: Towards Real-World Vision-Language Understanding

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: Data Construction: We strive to ensure our data is diverse, scalable and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content (expert knowledge, textbooks), aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction-tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Model Architecture: Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024) within a fixed token budget, while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. Training Strategy: We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. Starting with a focus on text, we gradually adjust the ratio to facilitate a balanced integration of both modalities.