deepseek-vl-1.3b-chat
Maintainer: deepseek-ai
45
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| Property | Value |
|---|---|
| Run this model | Run 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-1.3b-chat is a tiny vision-language model from DeepSeek AI. It uses the SigLIP-L as the vision encoder and is constructed based on the DeepSeek-LLM-1.3b-base model. The whole DeepSeek-VL-1.3b-base model is trained on around 400B vision-language tokens. deepseek-vl-1.3b-chat is an instructed version based on the deepseek-vl-1.3b-base model.
Similar models like deepseek-vl-7b-chat, DeepSeek-V2-Lite, and DeepSeek-Coder-V2-Lite-Instruct are also available from DeepSeek AI. These models vary in size, capabilities, and specific domains, but they all leverage the DeepSeek AI's expertise in building effective Mixture-of-Experts (MoE) language models.
Model inputs and outputs
Inputs
- Image: The model can process images of size 384x384 pixels.
- Text: The model can understand and respond to text-based prompts and conversations.
Outputs
- Text: The model can generate relevant and coherent text responses based on the provided image and text inputs.
Capabilities
deepseek-vl-1.3b-chat possesses general multimodal understanding capabilities, enabling it to process and understand a variety of content types, including logical diagrams, web pages, formula recognition, scientific literature, natural images, and embodied intelligence in complex scenarios.
What can I use it for?
The deepseek-vl-1.3b-chat model can be used for a wide range of vision and language understanding applications, such as image captioning, visual question answering, and multimodal dialogue systems. Its ability to process diverse content types makes it a versatile tool for tasks that require integrating visual and textual information.
Things to try
One interesting aspect of deepseek-vl-1.3b-chat is its potential for handling complex, multi-step scenarios that involve both visual and textual components. For example, you could try describing a step-by-step process depicted in a diagram or image and see how the model responds and guides you through the task.
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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