---
pipeline_tag: image-text-to-text
library_name: transformers
license: mit
---
# Skywork-R1V
## 📖 [Technical Report](https://github.com/SkyworkAI/Skywork-R1V/blob/main/Skywork_R1V.pdf) | 💻 [GitHub](https://github.com/SkyworkAI/Skywork-R1V) | 🌐 [ModelScope](https://modelscope.cn/models/Skywork/Skywork-R1V-38B)
[](https://github.com/SkyworkAI/Skywork-R1V/stargazers) [](https://github.com/SkyworkAI/Skywork-R1V/fork)
## 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
Comparison with Larger-Scale Open-Source and Closed-Source Models
|
Benchmark |
LLM |
VLM |
|
|
QwQ-32B-Preview |
InternVL-2.5-38B |
VILA 1.5-40B |
InternVL2-40B |
Skywork-R1V-38B |
| Reasoning |
MATH-500 |
90.6 |
- |
- |
- |
94.0 |
| AIME 2024 |
50.0 |
- |
- |
- |
72.0 |
| GPQA |
54.5 |
- |
- |
- |
61.6 |
| Vision |
MathVista(mini) |
- |
71.9 |
49.5 |
63.7 |
67.5 |
| MMMU(Val) |
- |
63.9 |
55.1 |
55.2 |
69.0 |
Evaluation results of state-of-the-art LLMs and VLMs
|
Vision |
Reasoning |
Vision |
|
|
MATH-500 |
AIME 2024 |
GPQA |
MathVista(mini) |
MMMU(Val) |
|
|
pass@1 |
pass@1 |
pass@1 |
pass@1 |
pass@1 |
| Qwen2.5-72B-Instruct |
❌ |
80.0 |
23.3 |
49.0 |
- |
- |
| Deepseek V3 |
❌ |
90.2 |
39.2 |
59.1 |
- |
- |
| Deepseek R1 |
❌ |
97.3 |
79.8 |
71.5 |
- |
- |
| Claude 3.5 Sonnet |
✅ |
78.3 |
16.0 |
65.0 |
65.3 |
66.4 |
| GPT-4o |
✅ |
74.6 |
9.3 |
49.9 |
63.8 |
69.1 |
| Kimi k1.5 |
✅ |
96.2 |
77.5 |
- |
74.9 |
70.0 |
| Qwen2.5-VL-72B-Instruct |
✅ |
- |
- |
- |
74.8 |
70.2 |
| LLaVA-Onevision-72B |
✅ |
- |
- |
- |
67.5 |
56.8 |
| InternVL2-Llama3-76B |
✅ |
- |
- |
- |
65.5 |
62.7 |
| InternVL2.5-78B |
✅ |
- |
- |
- |
72.3 |
70.1 |
| Skywork-R1V-38B |
✅ |
94.0 |
72.0 |
61.6 |
67.5 |
69.0 |
---
## 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:
```
@misc{peng2025skyworkr1vpioneeringmultimodal,
title={Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought},
author={Yi Peng and Chris and Xiaokun Wang and Yichen Wei and Jiangbo Pei and Weijie Qiu and Ai Jian and Yunzhuo Hao and Jiachun Pan and Tianyidan Xie and Li Ge and Rongxian Zhuang and Xuchen Song and Yang Liu and Yahui Zhou},
year={2025},
eprint={2504.05599},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.05599},
}
```
*This project is released under an open-source license.*
## Star History
[](https://www.star-history.com/#SkyworkAI/Skywork-R1V&Date)
``` 
