metadata
license: mit
pipeline_tag: image-text-to-text library_name: transformers license: mit
Skywork-R1V2
📖 R1V3 Report | 💻 GitHub
1. Model Introduction
Skywork-R1V3-38B is the latest and most powerful open-source multimodal reasoning model in the Skywork series, pushing the boundaries of cross-modal and cross-disciplinary intelligence.
With elaborate RL algorithm in the post-training stage, R1V3 significantly enhances multimodal reasoning ablity and achieves open-source state-of-the-art (SOTA) performance across multiple benchmarks.
🌟 Key Results
- MMMU: 76.0% — Open-source SOTA, approaching human experts (76.2)
- EMMA-Mini(CoT): 40.3 — Best in open source
- MMK12: 78.5 — Best in open source
- Physics Reasoning: PhyX-MC-TM (52.8), SeePhys (31.5) — Best in open source
- Logic Reasoning: MME-Reasoning (42.8) — Beats Claude-4-Sonnet, VisuLogic (28.5) — Best in open source
- Math Benchmarks: MathVista (77.1), MathVerse (59.6), MathVision (52.6) — Exceptional problem-solving
2. Evaluation
3. Usage
1. Clone the Repository
git clone https://github.com/SkyworkAI/Skywork-R1V.git
cd skywork-r1v/inference
2. Set Up the Environment
# For Transformers
conda create -n r1-v python=3.10 && conda activate r1-v
bash setup.sh
# For vLLM
conda create -n r1v-vllm python=3.10 && conda activate r1v-vllm
pip install -U vllm
3. Run the Inference Script
transformers inference
CUDA_VISIBLE_DEVICES="0,1" python inference_with_transformers.py \
--model_path path \
--image_paths image1_path \
--question "your question"
vllm inference
python inference_with_vllm.py \
--model_path path \
--image_paths image1_path image2_path \
--question "your question" \
--tensor_parallel_size 4
4. Citation
If you use Skywork-R1V in your research, please cite:
@misc{chris2025skyworkr1v2multimodalhybrid,
title={Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning},
author={Peiyu Wang and Yichen Wei and Yi Peng and Xiaokun Wang and Weijie Qiu and Wei Shen and Tianyidan Xie and Jiangbo Pei and Jianhao Zhang and Yunzhuo Hao and Xuchen Song and Yang Liu and Yahui Zhou},
year={2025},
eprint={2504.16656},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.16656},
}
@misc{peng2025skyworkr1vpioneeringmultimodal,
title={Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought},
author={Yi Peng and Peiyu Wang 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.