Instructions to use InfiX-ai/InfiGUI-G1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InfiX-ai/InfiGUI-G1-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="InfiX-ai/InfiGUI-G1-3B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("InfiX-ai/InfiGUI-G1-3B")
model = AutoModelForMultimodalLM.from_pretrained("InfiX-ai/InfiGUI-G1-3B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use InfiX-ai/InfiGUI-G1-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InfiX-ai/InfiGUI-G1-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InfiX-ai/InfiGUI-G1-3B",
		"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/InfiX-ai/InfiGUI-G1-3B

SGLang

How to use InfiX-ai/InfiGUI-G1-3B 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 "InfiX-ai/InfiGUI-G1-3B" \
    --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": "InfiX-ai/InfiGUI-G1-3B",
		"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 "InfiX-ai/InfiGUI-G1-3B" \
        --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": "InfiX-ai/InfiGUI-G1-3B",
		"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 InfiX-ai/InfiGUI-G1-3B with Docker Model Runner:
```
docker model run hf.co/InfiX-ai/InfiGUI-G1-3B
```

SiriusL commited on Aug 11, 2025

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@@ -166,6 +166,50 @@ To reproduce the results in our paper, please refer to our repo for detailed ins
 For more details on the methodology and evaluation, please refer to our [paper](https://arxiv.org/abs/2508.05731) and [repository](https://github.com/InfiXAI/InfiGUI-G1).
 ## Citation Information
 If you find this work useful, we would be grateful if you consider citing the following papers:

 For more details on the methodology and evaluation, please refer to our [paper](https://arxiv.org/abs/2508.05731) and [repository](https://github.com/InfiXAI/InfiGUI-G1).
+## Results
+Our InfiGUI-G1 models, trained with the AEPO framework, establish new state-of-the-art results among open-source models across a diverse and challenging set of GUI grounding benchmarks.
+### MMBench-GUI (L2) Results
+On the comprehensive MMBench-GUI benchmark, which evaluates performance across various platforms and instruction complexities, our InfiGUI-G1 models establish new state-of-the-art results for open-source models in their respective size categories.
+<div align="center">
+  <img src="https://raw.githubusercontent.com/InfiXAI/InfiGUI-G1/main/assets/results_mmbench-gui.png" width="90%" alt="MMBench-GUI Results">
+</div>
+### ScreenSpot-Pro Results
+On the challenging ScreenSpot-Pro benchmark, designed to test semantic understanding on high-resolution professional software, InfiGUI-G1 demonstrates significant improvements, particularly on icon-based grounding tasks. This highlights AEPO's effectiveness in enhancing semantic alignment by associating abstract visual symbols with their functions.
+<div align="center">
+  <img src="https://raw.githubusercontent.com/InfiXAI/InfiGUI-G1/main/assets/results_screenspot-pro.png" width="90%" alt="ScreenSpot-Pro Results">
+</div>
+### UI-Vision (Element Grounding) Results
+InfiGUI-G1 shows strong generalization capabilities on the UI-Vision benchmark, which is designed to test robustness across a wide variety of unseen desktop applications. Achieving high performance confirms that our AEPO framework fosters a robust understanding rather than overfitting to the training data.
+<div align="center">
+  <img src="https://raw.githubusercontent.com/InfiXAI/InfiGUI-G1/main/assets/results_ui-vision.png" width="90%" alt="UI-Vision Results">
+</div>
+### UI-I2E-Bench Results
+To further probe semantic reasoning, we evaluated on UI-I2E-Bench, a benchmark featuring a high proportion of implicit instructions that require reasoning beyond direct text matching. Our model's strong performance underscores AEPO's ability to handle complex, indirect commands.
+<div align="center">
+  <img src="https://raw.githubusercontent.com/InfiXAI/InfiGUI-G1/main/assets/results_i2e-bench.png" width="90%" alt="UI-I2E-Bench Results">
+</div>
+### ScreenSpot-V2 Results
+On the widely-used ScreenSpot-V2 benchmark, which provides comprehensive coverage across mobile, desktop, and web platforms, InfiGUI-G1 consistently outperforms strong baselines, demonstrating the broad applicability and data efficiency of our approach.
+<div align="center">
+  <img src="https://raw.githubusercontent.com/InfiXAI/InfiGUI-G1/main/assets/results_screenspot-v2.png" width="90%" alt="ScreenSpot-V2 Results">
+</div>
 ## Citation Information
 If you find this work useful, we would be grateful if you consider citing the following papers: