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
```

shawn0wang commited on Mar 17, 2025

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@@ -1,5 +1,9 @@
 # Skywork-R1V
 ## 1. Introduction
 We introduce Skywork-R1V, a multimodal reasoning model that extends the R1-series text models to visual modalities through a near-lossless transfer method. Using a lightweight visual projector, Skywork-R1V enables seamless multimodal adaptation without requiring retraining of either the base language model or vision encoder. To enhance visual-text alignment, we developed a hybrid optimization strategy combining Iterative Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO), significantly improving cross-modal integration. Additionally, we created an adaptive-length Chain-of-Thought distillation approach for generating reasoning data, which dynamically optimizes reasoning chain lengths to improve inference efficiency and prevent overthinking. The model achieves good performance on key multimodal reasoning benchmarks, scoring 69 on MMMU and 67.5 on MathVista, comparable to leading closed-source models like Gemini 2.0 and Kimi-k1.5. It also maintains strong textual reasoning capabilities, achieving impressive scores of 72.0 on AIME and 94.0 on MATH500.

 # Skywork-R1V
+<div align="center">
+  <img src="logo.jpeg" alt="Introduction Image" width="400" height="400">
+</div>
 ## 1. Introduction
 We introduce Skywork-R1V, a multimodal reasoning model that extends the R1-series text models to visual modalities through a near-lossless transfer method. Using a lightweight visual projector, Skywork-R1V enables seamless multimodal adaptation without requiring retraining of either the base language model or vision encoder. To enhance visual-text alignment, we developed a hybrid optimization strategy combining Iterative Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO), significantly improving cross-modal integration. Additionally, we created an adaptive-length Chain-of-Thought distillation approach for generating reasoning data, which dynamically optimizes reasoning chain lengths to improve inference efficiency and prevent overthinking. The model achieves good performance on key multimodal reasoning benchmarks, scoring 69 on MMMU and 67.5 on MathVista, comparable to leading closed-source models like Gemini 2.0 and Kimi-k1.5. It also maintains strong textual reasoning capabilities, achieving impressive scores of 72.0 on AIME and 94.0 on MATH500.