DeepSeek-VL: Towards Real-World Vision-Language Understanding
Submitted in March 2024. DeepSeek-VL is an open-source vision-language model that uses a hybrid vision encoder to process 1024x1024 high-resolution images and constructs instruction fine-tuning datasets based on real user scenarios. The model maintains language capabilities while achieving or surpassing state-of-the-art performance on multiple vision-language benchmarks at the same scale, with no degradation in language benchmarks. The 1.3B and 7B models are open-sourced.
DeepSeek-VL achieves high-resolution image understanding with low computational overhead through a carefully designed data strategy (covering real-world scenarios such as web screenshots, PDFs, OCR, charts, etc.) and a hybrid vision encoder. Its key innovation lies in integrating language model training from the pretraining stage and managing competition between vision and language modalities, thereby preserving strong language capabilities. This provides a new paradigm for building practical and efficient vision-language assistants.
The model uses a hybrid vision encoder combining low-resolution and high-resolution branches, efficiently processing 1024x1024 inputs. In the pretraining strategy, vision and language components are jointly trained from an early stage, and techniques such as dynamic learning rate adjustment are used to mitigate modality competition. Instruction fine-tuning data is generated based on real user scenario classification, covering various practical use cases. Experiments show that the 7B model outperforms similar models like LLaVA-1.5 on benchmarks such as MMBench and MME, while maintaining stable performance on language tasks (e.g., MMLU).
The open-source nature and high performance of this model make it an ideal foundation for building applications such as visual chatbots, document analysis, and OCR. For small and medium-sized enterprises, it enables low-cost deployment of private visual AI services. Additionally, its data construction methodology can be referenced by other fields, promoting the implementation of multimodal models in vertical industries.
It is recommended that enterprise AI platforms integrate DeepSeek-VL as a visual understanding module for automated document processing, image review, and other scenarios. Investment institutions can pay attention to the company DeepSeek behind it and the application ecosystem based on this model.
Future attention should be paid to the model's scalability in more complex scenarios such as video understanding and multi-turn dialogue, as well as contributions from the open-source community and derivative models. Furthermore, whether the hybrid vision encoder design can be further optimized to support higher resolution or real-time applications is worth exploring.