---
pipeline_tag: image-text-to-text
library_name: transformers
license: mit
---
# Skywork-R1V2
## 📖 [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-R1V2-38B | [InternViT-6B-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V2_5) | [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) | [🤗 Link](https://huggingface.co/Skywork/Skywork-R1V2-38B) |
## 2. Evaluation
Comparison with Larger-Scale Open-Source Models
Comparison with Properitary Models
| Model |
Supports Vision |
Text Reasoning (%) |
Multimodal Reasoning (%) |
|
|
AIME24 |
LiveCodebench |
liveBench |
IFEVAL |
BFCL |
MATH‑500 |
AIME 2024 |
GPQA |
MMMU(val) |
MathVista(mini) |
MathVision(mini) |
OlympiadBench |
mmmu‑pro |
|
|
% |
% |
% |
% |
% |
pass@1 |
pass@1 |
pass@1 |
% |
% |
% |
% |
% |
| R1V2‑38B |
✅ |
78.9 |
63.6 |
73.2 |
82.9 |
66.3 |
94.0 |
72.0 |
61.6 |
73.6 |
74.0 |
49.0 |
62.6 |
52.0 |
| R1V1‑38B |
✅ |
72.0 |
57.2 |
54.6 |
72.5 |
53.5 |
– |
– |
– |
68.0 |
67.0 |
– |
40.4 |
– |
| Deepseek‑R1‑671B |
❌ |
74.3 |
65.9 |
71.6 |
83.3 |
60.3 |
97.3 |
79.8 |
71.5 |
– |
– |
– |
– |
– |
| GPT‑o1 |
❌ |
79.8 |
63.4 |
72.2 |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
| GPT‑o4‑mini |
✅ |
93.4 |
74.6 |
78.1 |
– |
– |
74.6 |
9.3 |
49.9 |
81.6 |
84.3 |
58.0 |
– |
– |
| Claude 3.5 Sonnet |
✅ |
– |
– |
– |
– |
– |
78.3 |
16.0 |
65.0 |
66.4 |
65.3 |
– |
– |
– |
| Kimi k1.5 longcot |
✅ |
– |
– |
– |
– |
– |
96.2 |
77.5 |
– |
70.0 |
74.9 |
– |
– |
– |
| Qwen2.5‑VL‑72B‑Instruct |
✅ |
– |
– |
– |
– |
– |
– |
– |
– |
70.2 |
74.8 |
– |
– |
– |
| InternVL2.5‑78B |
✅ |
– |
– |
– |
– |
– |
– |
– |
– |
70.1 |
72.3 |
– |
33.2 |
– |
Evaluation results of state-of-the-art LLMs and VLMs
---
## 3. 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"
```
---
## 4. 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)
``` 
