Instructions to use inclusionAI/UI-Venus-2-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/UI-Venus-2-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="inclusionAI/UI-Venus-2-9B")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("inclusionAI/UI-Venus-2-9B")
model = AutoModelForMultimodalLM.from_pretrained("inclusionAI/UI-Venus-2-9B", device_map="auto")
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-2-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/UI-Venus-2-9B"
# 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-2-9B",
		"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-2-9B

SGLang

How to use inclusionAI/UI-Venus-2-9B 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-2-9B" \
    --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-2-9B",
		"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-2-9B" \
        --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-2-9B",
		"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-2-9B with Docker Model Runner:
```
docker model run hf.co/inclusionAI/UI-Venus-2-9B
```

syorami commited on 5 days ago

Commit

c96b5c2

verified ·

1 Parent(s): 65cfdff

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@@ -8,6 +8,10 @@ tags:
   - agent
 ---
 # UI-Venus-2-9B
 <div align="center">
@@ -34,6 +38,10 @@ UI-Venus-2 further integrates **safety-aware mechanisms** to ensure controlled e
 This repository releases the full-parameter weights of **UI-Venus-2-9B**, initialized from [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B), which achieves near state-of-the-art performance among models of comparable scale across GUI grounding, mobile, web, computer-use, and CAPTCHA benchmarks.
 ## Highlights
 - **Scaled multilingual mobile-use environments with reliable task generation.** A substantially expanded executable mobile pool covering Chinese and English app ecosystems, paired with a deep-research-driven query-generation strategy grounded in real application functionality.
@@ -49,6 +57,16 @@ UI-Venus-2 is trained on a deliberately complementary mixture of five task famil
 2. **Offline RL** — step-level RL trajectories for Mobile, OS, and Web optimize state-aware action selection and execution reliability, while verified CAPTCHA and grounding instances embedded in realistic interfaces teach precise localization under visual clutter.
 3. **Multi-teacher On-policy Distillation** — domain-specialized teachers are consolidated into a single unified policy that preserves the broad multimodal reasoning of the base model while composing spatial grounding, verified interaction, and long-horizon navigation.
 ## Quick Start
 ### Serving with vLLM

   - agent
 ---
+<div align="center">
+  <img src="assets/ui-venus-logo.png" width="140" alt="UI-Venus logo">
+</div>
 # UI-Venus-2-9B
 <div align="center">
 This repository releases the full-parameter weights of **UI-Venus-2-9B**, initialized from [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B), which achieves near state-of-the-art performance among models of comparable scale across GUI grounding, mobile, web, computer-use, and CAPTCHA benchmarks.
+<div align="center">
+  <img src="assets/performance_overview.png" width="100%" alt="Performance overview of UI-Venus-2 across GUI agent benchmarks">
+</div>
 ## Highlights
 - **Scaled multilingual mobile-use environments with reliable task generation.** A substantially expanded executable mobile pool covering Chinese and English app ecosystems, paired with a deep-research-driven query-generation strategy grounded in real application functionality.
 2. **Offline RL** — step-level RL trajectories for Mobile, OS, and Web optimize state-aware action selection and execution reliability, while verified CAPTCHA and grounding instances embedded in realistic interfaces teach precise localization under visual clutter.
 3. **Multi-teacher On-policy Distillation** — domain-specialized teachers are consolidated into a single unified policy that preserves the broad multimodal reasoning of the base model while composing spatial grounding, verified interaction, and long-horizon navigation.
+<div align="center">
+  <img src="assets/training_pipeline.png" width="90%" alt="The three-stage training pipeline of UI-Venus-2">
+</div>
+The training data is produced by a deep-research-driven task-generation pipeline and collected at scale across mobile (ADB, 200+ apps), browser (Playwright, 50k+ websites), and computer (PyAutoGUI / VM, 50+ desktop applications) environments:
+<div align="center">
+  <img src="assets/system_overview.png" width="100%" alt="System overview: task generation pipeline and trajectory collection across mobile, browser, and computer environments">
+</div>
 ## Quick Start
 ### Serving with vLLM