Instructions to use Skywork/Skywork-R1V-38B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skywork/Skywork-R1V-38B-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Skywork/Skywork-R1V-38B-AWQ")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Skywork/Skywork-R1V-38B-AWQ", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Skywork/Skywork-R1V-38B-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Skywork/Skywork-R1V-38B-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skywork/Skywork-R1V-38B-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Skywork/Skywork-R1V-38B-AWQ
- SGLang
How to use Skywork/Skywork-R1V-38B-AWQ with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Skywork/Skywork-R1V-38B-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skywork/Skywork-R1V-38B-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Skywork/Skywork-R1V-38B-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skywork/Skywork-R1V-38B-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Skywork/Skywork-R1V-38B-AWQ with Docker Model Runner:
docker model run hf.co/Skywork/Skywork-R1V-38B-AWQ
Update paper link and clean up model card
Browse filesThis PR improves the model card for Skywork-R1V-38B-AWQ by:
- Updating the "Technical Report" link to the official Hugging Face Papers page: [https://huggingface.co/papers/2507.06167](https://huggingface.co/papers/2507.06167).
- Correcting both English and Chinese BibTeX citations to reflect the paper title "Skywork-R1V3 Technical Report" and link to the same Hugging Face Papers URL.
- Removing the redundant duplicated Chinese sections (Usage, Hardware Requirements, Citation) as their content is a direct translation of the preceding English sections, improving overall conciseness and readability.
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---
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license: mit
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library_name: transformers
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pipeline_tag: image-text-to-text
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---
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<img src="skywork-logo.png" alt="Introduction Image" width="500" height="400">
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</div>
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## 📖 [Technical Report](https://
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<div align="center">
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```bibtex
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@article{skywork2025r1v,
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title = {Skywork
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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},
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year = {2025},
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journal = {https://
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url = {https://huggingface.co/
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}
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```
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# Skywork-R1V-38B-AWQ (中文说明)
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## 使用方法
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您可以使用不同的推理框架来使用这个量化模型:
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```bibtex
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@misc{peng2025skyworkr1vpioneeringmultimodal,
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title={Skywork
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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},
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year={2025},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2504.05599},
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}
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```
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---
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library_name: transformers
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license: mit
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pipeline_tag: image-text-to-text
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---
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<img src="skywork-logo.png" alt="Introduction Image" width="500" height="400">
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</div>
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## 📖 [Technical Report](https://huggingface.co/papers/2507.06167) | 💻 [GitHub](https://github.com/SkyworkAI/Skywork-R1V) | 🌐 [Wisemodel](https://wisemodel.cn/models/Skywork/Skywork-R1V)
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<div align="center">
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```bibtex
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@article{skywork2025r1v,
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title = {Skywork-R1V3 Technical Report},
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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},
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year = {2025},
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journal = {https://huggingface.co/papers/2507.06167},
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url = {https://huggingface.co/papers/2507.06167}
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}
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```
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# Skywork-R1V-38B-AWQ (中文说明)
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## 使用方法
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您可以使用不同的推理框架来使用这个量化模型:
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```bibtex
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@misc{peng2025skyworkr1vpioneeringmultimodal,
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title={Skywork-R1V3 Technical Report},
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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},
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year={2025},
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url={https://huggingface.co/papers/2507.06167},
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}
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```
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