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## Table of Contents
1. [Introduction](#1-introduction)
2. [Release](#2-release)
3. [Model Download](#3-model-download)
4. [Quick Start](#4-quick-start)
5. [License](#5-license)
6. [Citation](#6-citation)
<p align="center"> <p align="center">
<a href="https://github.com/deepseek-ai/DeepSeek-VL2/tree/main?tab=readme-ov-file#3-model-download"><b>📥 Model Download</b></a> | <a href="https://github.com/deepseek-ai/DeepSeek-VL2/tree/main?tab=readme-ov-file#3-model-download"><b>📥 Model Download</b></a> |
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<a href=""><b>👁️ Demo</b></a> <a href=""><b>👁️ Demo</b></a>
</p> </p>
## Table of Contents
1. [Introduction](#1-introduction)
2. [Release](#2-release)
3. [Model Download](#3-model-download)
4. [Quick Start](#4-quick-start)
5. [License](#5-license)
6. [Citation](#6-citation)
## 1. Introduction ## 1. Introduction
Introducing DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. Introducing DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively.