Instructions to use inclusionAI/UI-Venus-1.5-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/UI-Venus-1.5-2B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="inclusionAI/UI-Venus-1.5-2B")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("inclusionAI/UI-Venus-1.5-2B")
model = AutoModelForImageTextToText.from_pretrained("inclusionAI/UI-Venus-1.5-2B")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/UI-Venus-1.5-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/UI-Venus-1.5-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/UI-Venus-1.5-2B",
		"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/inclusionAI/UI-Venus-1.5-2B

SGLang

How to use inclusionAI/UI-Venus-1.5-2B 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 "inclusionAI/UI-Venus-1.5-2B" \
    --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": "inclusionAI/UI-Venus-1.5-2B",
		"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 "inclusionAI/UI-Venus-1.5-2B" \
        --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": "inclusionAI/UI-Venus-1.5-2B",
		"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 inclusionAI/UI-Venus-1.5-2B with Docker Model Runner:
```
docker model run hf.co/inclusionAI/UI-Venus-1.5-2B
```

zengw commited on Feb 11

Commit

bc4c6e6

verified ·

1 Parent(s): 40421b2

Update README.md

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@@ -12,7 +12,7 @@ This repository contains the UI-Venus model from the report [UI-Venus-1.5 Techni
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 [![Report](https://img.shields.io/badge/Report-Technical%20Report-blueviolet?logo=notion)](https://arxiv.org/abs/2602.09082)
-[![Website](https://img.shields.io/badge/🌐%20Website-UI--Venus--1.5-blue)]()
 [![GitHub](https://img.shields.io/badge/GitHub-Repository-green?logo=github)](https://github.com/inclusionAI/UI-Venus)
 [![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Model-orange?logo=huggingface)](https://huggingface.co/inclusionAI/UI-Venus-1.5-2B)
@@ -93,11 +93,14 @@ The model will be served at `http://localhost:8000/v1`.
 ## Citation
 Please consider citing if you find our work useful:
 ```plain
-@misc{uivenus15,
-      title={UI-Venus 1.5 Technical Report},
-      author={xxxx},
       year={2026},
-      note={Technical report coming soon}
 }
 @misc{gu2025uivenustechnicalreportbuilding,

 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 [![Report](https://img.shields.io/badge/Report-Technical%20Report-blueviolet?logo=notion)](https://arxiv.org/abs/2602.09082)
+[![Website](https://img.shields.io/badge/🌐%20Website-UI--Venus--1.5-blue)](https://ui-venus.github.io/UI-Venus-1.5)
 [![GitHub](https://img.shields.io/badge/GitHub-Repository-green?logo=github)](https://github.com/inclusionAI/UI-Venus)
 [![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Model-orange?logo=huggingface)](https://huggingface.co/inclusionAI/UI-Venus-1.5-2B)
 ## Citation
 Please consider citing if you find our work useful:
 ```plain
+@misc{venusteam2026uivenus15technicalreport,
+      title={UI-Venus-1.5 Technical Report},
+      author={Venus Team and Changlong Gao and Zhangxuan Gu and Yulin Liu and Xinyu Qiu and Shuheng Shen and Yue Wen and Tianyu Xia and Zhenyu Xu and Zhengwen Zeng and Beitong Zhou and Xingran Zhou and Weizhi Chen and Sunhao Dai and Jingya Dou and Yichen Gong and Yuan Guo and Zhenlin Guo and Feng Li and Qian Li and Jinzhen Lin and Yuqi Zhou and Linchao Zhu and Liang Chen and Zhenyu Guo and Changhua Meng and Weiqiang Wang},
       year={2026},
+      eprint={2602.09082},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2602.09082},
 }
 @misc{gu2025uivenustechnicalreportbuilding,