---
pipeline_tag: image-text-to-text
---
# Skywork-R1V
<div align="center">
<img src="logo.jpeg" alt="Introduction Image" width="400" height="400">
</div>
## 🌐 [Homepage](#) | 📖 [Technical Report](https://github.com/SkyworkAI/Skywork-R1V/blob/main/Skywork_R1V.pdf) | 💻 [GitHub](https://github.com/SkyworkAI/Skywork-R1V)
---
## 1. Model Introduction
| Model Name | Vision Encoder | Language Model | HF Link |
| ---------------------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | ------------ |
| Skywork-R1V-38B | [InternViT-6B-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V2_5) | [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) | [🤗 Link](#) |
| Skywork-R1V-38B-qwq | [InternViT-6B-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V2_5) | [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) | - |
## 2. Feature
- **Visual Chain-of-Thought**: Enables multi-step logical reasoning on visual inputs, breaking down complex image-based problems into manageable steps.
- **Mathematical & Scientific Analysis**: Capable of solving visual math problems and interpreting scientific/medical imagery with high precision.
- **Cross-Modal Understanding**: Seamlessly integrates text and images for richer, context-aware comprehension.
## 3. Evaluation
<br>
<br>
<div align="center">
<b>Comparison with Larger-Scale Open-Source and Closed-Source Models</b>
</div>
<table align="center">
<thead>
<tr>
<th></th>
<th align="center"><strong>Benchmark</strong></th>
<th align="center"><strong>LLM</strong></th>
<th align="center" colspan="4"><strong>VLM</strong></th>
</tr>
<tr>
<th></th>
<th></th>
<th align="center"><strong>QwQ-32B-Preview</strong></th>
<th align="center"><strong>InternVL-2.5-38B</strong></th>
<th align="center"><strong>VILA 1.5-40B</strong></th>
<th align="center"><strong>InternVL2-40B</strong></th>
<th align="center"><strong>Skywork-R1V-38B</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">Reasoning</td>
<td>MATH-500</td>
<td align="center">90.6</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center"><strong>94.0</strong></td>
</tr>
<tr>
<td>AIME 2024</td>
<td align="center">50.0</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center"><strong>72.0</strong></td>
</tr>
<tr>
<td>GPQA</td>
<td align="center">54.5</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center"><strong>61.6</strong></td>
</tr>
<tr>
<td rowspan="3">Vision</td>
<td>MathVista(mini)</td>
<td align="center">-</td>
<td align="center">71.9</td>
<td align="center">49.5</td>
<td align="center">63.7</td>
<td align="center">67.5</td>
</tr>
<tr>
<td>MMMU(Val)</td>
<td align="center">-</td>
<td align="center">63.9</td>
<td align="center">55.1</td>
<td align="center">55.2</td>
<td align="center"><strong>69.0</strong></td>
</tr>
</tbody>
</table>
<br>
<br>
<div align="center">
<b>Evaluation results of state-of-the-art LLMs and VLMs</b>
</div>
<table>
<thead>
<tr>
<th></th>
<th align="center"><strong>Vision</strong></th>
<th align="center" colspan="3"><strong>Reasoning</strong></th>
<th align="center" colspan="3"><strong>Vision</strong></th>
</tr>
<tr>
<th></th>
<th></th>
<th align="center"><strong>MATH-500</strong></th>
<th align="center"><strong>AIME 2024</strong></th>
<th align="center"><strong>GPQA</strong></th>
<th align="center"><strong>MathVista(mini)</strong></th>
<th align="center"><strong>MMMU(Val)</strong></th>
</tr>
<tr>
<th></th>
<th></th>
<th align="center">pass@1</th>
<th align="center">pass@1</th>
<th align="center">pass@1</th>
<th align="center">pass@1</th>
<th align="center">pass@1</th>
</tr>
</thead>
<tbody>
<tr>
<td>Qwen2.5-72B-Instruct</td>
<td align="center">❌</td>
<td align="center">80.0</td>
<td align="center">23.3</td>
<td align="center">49.0</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td>Deepseek V3</td>
<td align="center">❌</td>
<td align="center">90.2</td>
<td align="center">39.2</td>
<td align="center">59.1</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td>Deepseek R1</td>
<td align="center">❌</td>
<td align="center">97.3</td>
<td align="center">79.8</td>
<td align="center">71.5</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td>Claude 3.5 Sonnet</td>
<td align="center">✅</td>
<td align="center">78.3</td>
<td align="center">16.0</td>
<td align="center">65.0</td>
<td align="center">65.3</td>
<td align="center">66.4</td>
</tr>
<tr>
<td>GPT-4o</td>
<td align="center">✅</td>
<td align="center">74.6</td>
<td align="center">9.3</td>
<td align="center">49.9</td>
<td align="center">63.8</td>
<td align="center">69.1</td>
</tr>
<tr>
<td>Kimi k1.5</td>
<td align="center">✅</td>
<td align="center">96.2</td>
<td align="center">77.5</td>
<td align="center">-</td>
<td align="center">74.9</td>
<td align="center">70.0</td>
</tr>
<tr>
<td>Qwen2.5-VL-72B-Instruct</td>
<td align="center">✅</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">74.8</td>
<td align="center">70.2</td>
</tr>
<tr>
<td>LLaVA-Onevision-72B</td>
<td align="center">✅</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">67.5</td>
<td align="center">56.8</td>
</tr>
<tr>
<td>InternVL2-Llama3-76B</td>
<td align="center">✅</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">65.5</td>
<td align="center">62.7</td>
</tr>
<tr>
<td>InternVL2.5-78B</td>
<td align="center">✅</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">72.3</td>
<td align="center">70.1</td>
</tr>
<tr>
<td>Skywork-R1V-38B</td>
<td align="center">✅</td>
<td align="center">94.0</td>
<td align="center">72.0</td>
<td align="center">61.6</td>
<td align="center">67.5</td>
<td align="center">69.0</td>
</tr>
</tbody>
</table>
<div align="center">
<img src="eval.jpeg" width="90%" height="90%" alt="skywork_r1v_eval" />
</div>
---
## 4. Usage
### 1. Clone the Repository
```shell
git clone https://github.com/SkyworkAI/Skywork-R1V.git
cd skywork-r1v/inference
```
### 2. Set Up the Environment
```shell
conda create -n r1-v python=3.10
conda activate r1-v
bash setup.sh
```
### 3. Run the Inference Script
```shell
CUDA_VISIBLE_DEVICES="0,1" python inference_with_transformers.py \
--model_path path \
--image_paths image1_path \
--question "your question"
```
---
## 5. Citation
If you use Skywork-R1V in your research, please cite:
```
@article{skywork2025r1v,
title = {Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought},
author = {Yi Peng, Chris, Xiaokun Wang, Yichen Wei, Jiangbo Pei, Weijie Qiu, Ai Jian, Yunzhuo Hao, Jiachun Pan, Tianyidan Xie, Li Ge, Rongxian Zhuang, Xuchen Song, Yang Liu, Yahui Zhou},
year = {2025},
journal = {https://github.com/SkyworkAI/Skywork-R1V/blob/main/Skywork_R1V.pdf},
url = {https://huggingface.co/Skywork/Skywork-R1V-38B}
}
```
*This project is released under an open-source license.*