Instructions to use inclusionAI/UI-Venus-Ground-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/UI-Venus-Ground-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="inclusionAI/UI-Venus-Ground-7B")
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-Ground-7B")
model = AutoModelForImageTextToText.from_pretrained("inclusionAI/UI-Venus-Ground-7B")
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-Ground-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/UI-Venus-Ground-7B"
# 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-Ground-7B",
		"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-Ground-7B

SGLang

How to use inclusionAI/UI-Venus-Ground-7B 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-Ground-7B" \
    --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-Ground-7B",
		"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-Ground-7B" \
        --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-Ground-7B",
		"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-Ground-7B with Docker Model Runner:
```
docker model run hf.co/inclusionAI/UI-Venus-Ground-7B
```

zengw commited on Aug 19, 2025

Commit

cfc2528

verified ·

1 Parent(s): 3a7156b

Update README.md

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README.md +9 -4

README.md CHANGED Viewed

@@ -1,8 +1,13 @@
 ---
 license: apache-2.0
 ---
 ### UI-Venus
-This repository contains the UI-Venus model from the report [UI-Venus: Building High-performance UI Agents with RFT](https://arxiv.org/abs/2508.10833). UI-Venus is a native UI agent based on the Qwen2.5-VL multimodal large language model, designed to perform precise GUI element grounding and effective navigation using only screenshots as input. It achieves state-of-the-art performance through Reinforcement Fine-Tuning (RFT) with high-quality training data. More inference details and usage guides are available in the GitHub repository. We will continue to update results on standard benchmarks including Screenspot-v2/Pro and AndroidWorld.
@@ -39,7 +44,7 @@ Key innovations include:
 ---
-##  Installation
 First, install the required dependencies:
@@ -50,7 +55,7 @@ pip install transformers==4.49.0 qwen-vl-utils
-##  Quick Start
 Use the shell scripts to launch the evaluation. The evaluation setup follows the same protocol as **ScreenSpot**, including data format, annotation structure, and metric calculation.
@@ -150,7 +155,7 @@ def inference(instruction, image_path):
     return result_dict
 ```
 ---
-###  Results on ScreenSpot-v2
 | **Model**                | **Mobile Text** | **Mobile Icon** | **Desktop Text** | **Desktop Icon** | **Web Text** | **Web Icon** | **Avg.** |
 |--------------------------|-----------------|-----------------|------------------|------------------|--------------|--------------|----------|

 ---
 license: apache-2.0
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 ### UI-Venus
+This repository contains the UI-Venus model from the report [UI-Venus Technical Report: Building High-performance UI Agents with RFT](https://arxiv.org/abs/2508.10833).
+UI-Venus is a native UI agent based on the Qwen2.5-VL multimodal large language model, designed to perform precise GUI element grounding and effective navigation using only screenshots as input. It achieves state-of-the-art performance through Reinforcement Fine-Tuning (RFT) with high-quality training data. More inference details and usage guides are available in the GitHub repository. We will continue to update results on standard benchmarks including Screenspot-v2/Pro and AndroidWorld.
 ---
+## Installation
 First, install the required dependencies:
+## Quick Start
 Use the shell scripts to launch the evaluation. The evaluation setup follows the same protocol as **ScreenSpot**, including data format, annotation structure, and metric calculation.
     return result_dict
 ```
 ---
+### Results on ScreenSpot-v2
 | **Model**                | **Mobile Text** | **Mobile Icon** | **Desktop Text** | **Desktop Icon** | **Web Text** | **Web Icon** | **Avg.** |
 |--------------------------|-----------------|-----------------|------------------|------------------|--------------|--------------|----------|