deepseek-vl-7b-chat
Maintainer: deepseek-ai
191
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
deepseek-vl-7b-chat is an instructed version of the deepseek-vl-7b-base model, which is an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. The deepseek-vl-7b-base model uses the SigLIP-L and SAM-B as the hybrid vision encoder, and is constructed based on the deepseek-llm-7b-base model, which is trained on an approximate corpus of 2T text tokens. The whole deepseek-vl-7b-base model is finally trained around 400B vision-language tokens.
The deepseek-vl-7b-chat model is an instructed version of the deepseek-vl-7b-base model, making it capable of engaging in real-world vision and language understanding applications, including processing logical diagrams, web pages, formula recognition, scientific literature, natural images, and embodied intelligence in complex scenarios.
Model inputs and outputs
Inputs
- Image: The model can take images as input, supporting a resolution of up to 1024 x 1024.
- Text: The model can also take text as input, allowing for multimodal understanding and interaction.
Outputs
- Text: The model can generate relevant and coherent text responses based on the provided image and/or text inputs.
- Bounding Boxes: The model can also output bounding boxes, enabling it to localize and identify objects or regions of interest within the input image.
Capabilities
deepseek-vl-7b-chat has impressive capabilities in tasks such as visual question answering, image captioning, and multimodal understanding. For example, the model can accurately describe the content of an image, answer questions about it, and even draw bounding boxes around relevant objects or regions.
What can I use it for?
The deepseek-vl-7b-chat model can be utilized in a variety of real-world applications that require vision and language understanding, such as:
- Content Moderation: The model can be used to analyze images and text for inappropriate or harmful content.
- Visual Assistance: The model can help visually impaired users by describing images and answering questions about their contents.
- Multimodal Search: The model can be used to develop search engines that can understand and retrieve relevant information from both text and visual sources.
- Education and Training: The model can be used to create interactive educational materials that combine text and visuals to enhance learning.
Things to try
One interesting thing to try with deepseek-vl-7b-chat is its ability to engage in multi-round conversations about images. By providing the model with an image and a series of follow-up questions or prompts, you can explore its understanding of the visual content and its ability to reason about it over time. This can be particularly useful for tasks like visual task planning, where the model needs to comprehend the scene and take multiple steps to achieve a goal.
Another interesting aspect to explore is the model's performance on specialized tasks like formula recognition or scientific literature understanding. By providing it with relevant inputs, you can assess its capabilities in these domains and see how it compares to more specialized models.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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