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
```

UI-Venus-2-9B

UI-Venus-2 is a general-purpose foundation GUI agent designed to operate across mobile applications, web platforms, and desktop operating systems through a unified closed-loop reasoning–action framework: the agent observes the current interface, reasons about the task state, executes an action, and incorporates environmental feedback into its next decision.

To bridge the gap from benchmark-oriented models toward dependable real-world applications, UI-Venus-2 jointly scales three critical dimensions:

Environments — expanding coverage to 170+ multilingual mobile apps (100+ Chinese and 70+ English), 50k+ live websites, and native desktop OS with 50+ professional applications;
Tasks — a deep-research pipeline that grounds generated instructions in the actual functionality of each application, improving the accuracy, validity, and executability of task queries;
Verification — trace-level and sample-level evaluators based on task-relevant visual keypoints and multi-model voting, providing reliable RL reward signals that are robust to reward hacking.

UI-Venus-2 further integrates safety-aware mechanisms to ensure controlled execution of consequential actions, bringing the attack success rate on OSBlind down from 90%+ (typical of prior GUI agents) to 12.3%.

This repository releases the full-parameter weights of UI-Venus-2-9B, initialized from 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.

Performance overview of UI-Venus-2 across GUI agent 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.
Computer-use capability built from the ground up. Dedicated desktop operating-system capabilities constructed from scratch through computer-use data collection and task-specific training, extending the UI-Venus family to mobile, web, and OS interaction in one unified end-to-end agent.
Keypoint-grounded verification. Task completion is judged on task-relevant visual keypoints rather than a coarse holistic look at the final screen, with multi-model voting aggregating heterogeneous judges — reducing single-judge bias and making the reward signal robust to reward hacking.
Verification-augmented reflection. Verified feedback is distilled back into training as reflection supervision, so the agent can distinguish partial progress from true completion, avoid premature termination, and recover during long-horizon interaction.

Training Pipeline

UI-Venus-2 is trained on a deliberately complementary mixture of five task families — Grounding, CAPTCHA, Mobile, Web, and Computer — through three stages:

Multimodal Mid-Training — large-scale trajectory-based mid-training over simulated mobile, web, and OS environments, with human–discriminator collaborative verification filtering invalid or ambiguous interactions, plus programmatically synthesized grounding and CAPTCHA supervision.
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.
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.

The three-stage training pipeline of UI-Venus-2

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:

System overview: task generation pipeline and trajectory collection across mobile, browser, and computer environments

Quick Start

Serving with vLLM

pip install -U vllm

vllm serve inclusionAI/UI-Venus-2-9B \
    --port 8000 \
    --tensor-parallel-size 1 \
    --max-model-len 262144 \
    --reasoning-parser qwen3

The server exposes an OpenAI-compatible API at http://localhost:8000/v1.

For the full agent scaffolding — grounding / navigation prompts, the action space, ADB / Playwright / VM deployment, and evaluation infrastructure — please refer to our GitHub repository.

Results

Key results per domain, with strong published baselines for context. A dash denotes a result not currently reported. Best result per benchmark in bold. Full comparison tables and metric definitions are available in the technical report.

Mobile Use

Success rate (%) on online, interactive mobile-agent benchmarks.

Model	AndroidWorld	MobileWorld	VenusBench-Mobile
UI-Venus-2-9B (ours)	80.2	65.8	46.5
UI-Venus-2-27B	84.0	76.1	48.7
UI-Venus-1.5-30B-A3B	77.6	—	21.5
Qwen-UI-Agent-27B	—	82.1	—
Qwen3.6-27B	70.3	—	28.0
MAI-UI-8B	70.7	—	—
GUI-Owl-1.5-32B-Instruct	69.8	—	—
Claude Opus 4.6	—	44.5	36.5
Kimi K2.6	—	55.6	31.2
Seed2.0 Pro	—	63.2	—

Computer Use

Success rate (%) on open-ended tasks in real desktop environments.

Model	OSWorld-Verified	DeskCraft
UI-Venus-2-9B (ours)	70.8	48.0
UI-Venus-2-27B	80.5	55.5
Qwen-UI-Agent-27B	79.5	—
Kimi K2.6	73.1	41.4
Qwen3.6-27B	62.0	28.7
GUI-Owl-1.5-32B-Instruct	56.5	—
Seed2.0 Pro	—	40.0
Qwen3.5-9B	—	14.6

Web Navigation

Success rate (%) on end-to-end web navigation; Odysseys reports the average rubric score (%).

Model	WebVoyager	Online-Mind2Web	REAL	Odysseys
UI-Venus-2-9B (ours)	90.8	74.0	76.9	77.3
UI-Venus-2-27B	93.4	78.3	80.2	80.4
UI-Venus-1.5-30B-A3B	—	—	38.0	—
Fara-1.5-27B	89.3	72.3	—	—
GLM-5V-Turbo	88.5	—	—	—
Claude Opus 4.6	88.0	—	—	68.9
Seed-2.0	85.1	68.5	74.4	60.2
UI-TARS-1.5	—	75.8	—	—
OpenAI Operator	—	61.3	—	—
Kimi K2.6	—	—	74.4	—

GUI Grounding

Accuracy (%) on element localization across high-resolution professional software, mobile / web / desktop interfaces, and reasoning-heavy instructions.

Model	ScreenSpot-Pro	VenusBench-GD	OSWorld-G-R	UI-Vision
UI-Venus-2-9B (ours)	73.0	77.1	78.5	53.2
UI-Venus-2-27B	74.1	80.1	79.1	66.9
UI-Venus-1.5-30B-A3B	—	75.0	76.4	54.7
UI-Venus-1.0-72B	—	70.2	—	—
Qwen-UI-Agent-27B	—	—	78.5	70.0
Qwen 3.7 Plus	68.9	—	78.2	68.0
MAI-UI-32B	67.9	—	—	—
Holo2-30B-A3B	66.1	59.5	—	—
Seed 2.1 Pro	65.3	—	78.0	62.0
Step-GUI-4B	—	54.6	—	—

CAPTCHA Solving

Pass@1 (%) across eight real-world CAPTCHA families — slider puzzles, image rotation, text and icon click, OCR entry, and visual reasoning — emitting the full multi-action sequence in a single turn.

Model	Spatial-CAPTCHA-Bench	MCA-Bench	NextGen-CAPTCHAs	OpenCaptcha
UI-Venus-2-9B (ours)	42.8	75.7	47.6	50.7
UI-Venus-2-27B	48.6	79.6	54.5	56.3
Doubao-Seed-2.0-Pro	43.6	35.5	20.4	55.6
Qwen3.6-27B	31.0	51.7	14.1	47.7
Kimi K2.6	24.8	38.7	7.2	47.8
Claude Opus 4.6	9.5	—	—	—
Qwen3.5-9B	—	30.4	2.8	36.4

Safety

OSBlind evaluates susceptibility to safety blind spots: benign-looking instructions in realistic desktop environments that can lead to unintended harmful outcomes, measured by Attack Success Rate (ASR, lower is better).

Model	OSBlind ASR (%) ↓
UI-Venus-2-27B	12.3
UI-Venus-2-9B (ours)	18.5
Claude 4.5 Sonnet	73.0
OpenCUA-32B	91.8
GPT-4o	93.3
UI-TARS-1.5-7B	95.2

Citation

If you find UI-Venus-2 useful, please cite our technical reports:

@article{uivenus2026,
  title   = {UI-Venus-2 Technical Report},
  author  = {{Venus Team, Ant Group}},
  journal = {arXiv preprint arXiv:XXXX.XXXXX},
  year    = {2026}
}

@article{uivenus15,
  title   = {UI-Venus-1.5 Technical Report},
  author  = {{Venus Team, Ant Group}},
  journal = {arXiv preprint arXiv:2602.09082},
  year    = {2026}
}

@article{uivenus2025,
  title   = {UI-Venus Technical Report: Building High-performance UI Agents with RFT},
  author  = {{Venus Team, Ant Group}},
  journal = {arXiv preprint arXiv:2508.10833},
  year    = {2025}
}

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Papers for inclusionAI/UI-Venus-2-9B

UI-Venus-1.5 Technical Report

Paper • 2602.09082 • Published Feb 9 • 157

UI-Venus Technical Report: Building High-performance UI Agents with RFT

Paper • 2508.10833 • Published Aug 14, 2025 • 46