Instructions to use Skywork/R1V4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skywork/R1V4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Skywork/R1V4")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Skywork/R1V4", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Skywork/R1V4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Skywork/R1V4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skywork/R1V4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Skywork/R1V4
- SGLang
How to use Skywork/R1V4 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/R1V4" \ --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/R1V4", "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/R1V4" \ --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/R1V4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Skywork/R1V4 with Docker Model Runner:
docker model run hf.co/Skywork/R1V4
Skywork-R1V4
1. Model Introduction
Skywork-R1V4 is a 30B (A3B) multimodal agent that unifies:
- Multimodal task planning
- Active image manipulation (“thinking with images”)
- Deep multimodal search (text × image)
- Interleaved tool-grounded reasoning
Skywork-R1V4 is trained purely via supervised finetuning on < 30k high-quality, execution-consistent trajectories.
At inference time, the model exhibits emergent long-horizon reasoning, executing 10+ tool calls across visual operations and web search to solve complex real-world tasks.
Skywork-R1V4 achieves state-of-the-art performance on multimodal search benchmarks:
- MMSearch: 66.1
- FVQA: 67.2
- Beats Gemini 2.5 Flash on all 11 comparable metrics
2. Feature
🔍 “Thinking With Images”
Skywork-R1V4 actively manipulates images through:
• Multi-stage cropping
• Local detail extraction
• Region attention
• Visual clue refinement
🔄 Interleaved Reasoning
The model alternates between:
1. Visual reasoning
2. Image operation
3. Web search
4. Cross-evidence verification
3. Links
- Model Center: https://platform.skyworkmodel.ai/#/model-center
- API Documentation (R1V4): https://docs.skyworkmodel.ai/r1v4/api-reference/completions.html
4. Citation
If you use Skywork-R1V4 in your research, please cite:
@misc{zhang2025skyworkr1v4agenticmultimodalintelligence,
title={Skywork-R1V4: Toward Agentic Multimodal Intelligence through Interleaved Thinking with Images and DeepResearch},
author={Yifan Zhang and Liang Hu and Haofeng Sun and Peiyu Wang and Yichen Wei and Shukang Yin and Jiangbo Pei and Wei Shen and Peng Xia and Yi Peng and Tianyidan Xie and Eric Li and Yang Liu and Xuchen Song and Yahui Zhou},
year={2025},
eprint={2512.02395},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2512.02395},
}
@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},
}