Instructions to use Skywork/Skywork-R1V-38B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Skywork/Skywork-R1V-38B 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", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Skywork/Skywork-R1V-38B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Skywork/Skywork-R1V-38B 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"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Skywork/Skywork-R1V-38B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Skywork/Skywork-R1V-38B

SGLang

How to use Skywork/Skywork-R1V-38B 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" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Skywork/Skywork-R1V-38B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Skywork/Skywork-R1V-38B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Skywork/Skywork-R1V-38B with Docker Model Runner:
```
docker model run hf.co/Skywork/Skywork-R1V-38B
```

Skywork-R1V-38B / README.md

nielsr HF Staff

Update library name

debdb19 verified about 1 year ago

9.23 kB

pipeline_tag: image-text-to-text
library_name: vllm
license: mit

Skywork-R1V

📖 Technical Report | 💻 GitHub | 🌐 ModelScope

1. Model Introduction

Model Name	Vision Encoder	Language Model	HF Link
Skywork-R1V-38B	InternViT-6B-448px-V2_5	deepseek-ai/DeepSeek-R1-Distill-Qwen-32B	🤗 Link
Skywork-R1V-38B-qwq	InternViT-6B-448px-V2_5	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

git clone https://github.com/SkyworkAI/Skywork-R1V.git
cd skywork-r1v/inference

2. Set Up the Environment

conda create -n r1-v python=3.10
conda activate r1-v
bash setup.sh

3. Run the Inference Script

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

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