deepseek-vl-7b-base
Maintainer: lucataco
3
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Model overview
DeepSeek-VL is an open-source Vision-Language (VL) model designed for real-world vision and language understanding applications. Developed by the team at DeepSeek AI, the model possesses general multimodal understanding capabilities, allowing it to process logical diagrams, web pages, formula recognition, scientific literature, natural images, and even embodied intelligence in complex scenarios.
Similar models include moondream2, a small vision language model designed for edge devices, llava-13b, a large language and vision model with GPT-4 level capabilities, and phi-3-mini-4k-instruct, a lightweight, state-of-the-art open model trained with the Phi-3 datasets.
Model inputs and outputs
The DeepSeek-VL model accepts a variety of inputs, including images, text prompts, and conversations. It can generate responses that combine visual and language understanding, making it suitable for a wide range of applications.
Inputs
- Image: An image URL or file that the model will analyze and incorporate into its response.
- Prompt: A text prompt that provides context or instructions for the model to follow.
- Max New Tokens: The maximum number of new tokens the model should generate in its response.
Outputs
- Response: A generated response that combines the model's visual and language understanding to address the provided input.
Capabilities
The DeepSeek-VL model excels at tasks that require multimodal reasoning, such as image captioning, visual question answering, and document understanding. It can analyze complex scenes, recognize logical diagrams, and extract information from scientific literature. The model's versatility makes it suitable for a variety of real-world applications.
What can I use it for?
DeepSeek-VL can be used for a wide range of applications that require vision-language understanding, such as:
- Visual question answering: Answering questions about the content and context of an image.
- Image captioning: Generating detailed descriptions of images.
- Multimodal document understanding: Extracting information from documents that combine text and images, such as scientific papers or technical manuals.
- Logical diagram understanding: Analyzing and understanding the content and structure of logical diagrams, such as those used in engineering or mathematics.
Things to try
Experiment with the DeepSeek-VL model by providing it with a diverse range of inputs, such as images of different scenes, diagrams, or scientific documents. Observe how the model combines its visual and language understanding to generate relevant and informative responses. Additionally, try using the model in different contexts, such as educational or industrial applications, to explore its versatility and potential use cases.
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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