DeepSeek-VL2

What is DeepSeek-VL2?

DeepSeek-VL2 is a state-of-the-art DeepSeek developed by a local Chinese company.visual language model. It is based on the Mixed of Experts (MoE) architecture, which aims to enhance the performance of AI in complex real-world applications through multimodal understanding capabilities.

DeepSeek-VL2 introduces a dynamic chunked visual coding strategy for the visual component, which is capable of efficiently processing high-resolution images, and utilizes the DeepSeekMoE model with a multi-head potential attention mechanism for the linguistic component, which achieves efficient inference and high throughput. The model demonstrates excellent capabilities in a variety of tasks such as visual question and answer, optical character recognition, document/table/diagram comprehension, and visual localization.

DeepSeek-VL2 is now open source!DeepSeek's official public website has published a blog post announcing the open-source DeepSeek-VL2 model and saying that its visual model has officially entered the era of Mixture of Experts (MoE).DeepSeek-VL2 is the newest open-source MoE visual language model, which includes functions such as visual question answering, optical character recognition, document/form/diagram understanding, and visual localization. table/diagram comprehension, and visual localization. Currently, it is available in DeepSeek-VL2-Tiny, DeepSeek-VL2-Small, and DeepSeek-VL2 versions with 1.0B, 2.8B, and 4.5B activation parameters, respectively.

DeepSeek-VL2 Technical Features

1. Mixed Expertise (MoE) Architecture::

   • DeepSeek-VL2 employs a Mixture of Experts (MoE) architecture, which allows the model to scale in parameter size while effectively controlling the computational cost.
   • By introducing strategies such as expert parallelism, efficient training is achieved and the performance and scalability of the model is improved.

2. Dynamic high-resolution visual coding::

   • DeepSeek-VL2 introduces a dynamic slice visual coding strategy that is capable of processing high-resolution images with different aspect ratios, ensuring that high-resolution images are processed without losing key details.
   • This technique is well suited for tasks such as document analysis and visual localization.

3. Multiple Potential Attention Mechanisms::

   • This mechanism allows the model to efficiently process large amounts of textual data, reducing the computational overhead associated with processing dense linguistic input.

4. Quality training data::

   • The training of DeepSeek-VL2 covers diverse multimodal datasets, introducing new capabilities such as terse map understanding, visual localization, and visual story generation.
   • Twice as much high-quality training data as the previous generation of DeepSeek-VL, allowing the model to excel in a wide range of tasks.

DeepSeek-VL2 modeling variant

The DeepSeek-VL2 series offers three variants with different parameter configurations to meet the needs of different users:

1. DeepSeek-VL2-Tiny: With 3.37 billion parameters (100 million active parameters), it is suitable for application scenarios where resources are limited or rapid deployment is required.
2. DeepSeek-VL2-Small: With 16.1 billion parameters (280 million active parameters), it reduces computational requirements while maintaining high performance.
3. DeepSeek-VL2: The parameters are not explicitly labeled, but can be presumed to be higher parameter configurations, suitable for application scenarios with higher requirements for performance and accuracy.

DeepSeek-VL2 Application Scenarios

DeepSeek-VL2 is capable of being used for a wide range of tasks, including but not limited to:

1. Visual Q&A: The model is able to understand what is going on in the image and give accurate answers to questions.
2. optical character recognition, OCR: The model recognizes the text in the image and converts it into editable text.
3. Document/table/chart comprehension: The model parses information from documents, tables and charts to extract key data.
4. visual orientation: The model is able to accurately localize the target object in the image.

In addition, DeepSeek-VL2 has also demonstrated strong application capabilities in the financial sector. For example, several banks have successfully localized and deployed DeepSeek-VL2 multimodal models for intelligentContract management, intelligent risk control, asset custody and valuation reconciliation, customer service assistant, think tank and many other scenarios. These applications significantly improve business efficiency and accuracy and reduce operating costs.

DeepSeek-VL2 Memory Requirements and Graphics Card Recommendations

1. VGA memory requirements::

   • DeepSeek-VL2 is the version with the most parameters in the series, with more demanding video memory requirements. Expect to need at least 16GB of video memory to run smoothly, especially when reasoning.

2. Video Card Recommendations::

   • For the DeepSeek-VL2-Tiny version, an 8GB memory graphics card such as the NVIDIA RTX 3060 or RTX 3070 will suffice for basic reasoning.
   • For DeepSeek-VL2-Small and DeepSeek-VL2 versions, it is recommended to choose RTX 3080, RTX 4080 or RTX 4090 class graphics cards. These cards provide higher computing power and video memory to accommodate the inference needs of large-scale models.

Open source address:https://github.com/deepseek-ai/DeepSeek-VL2
Paper Address:https://github.com/deepseek-ai/DeepSeek-VL2/blob/main/DeepSeek_VL2_paper.pdf
Demo Address:https://huggingface.co/spaces/deepseek-ai/deepseek-vl2-small