Instructions to use microsoft/GELab-Zero-4B-preview-Sico-Evolution with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/GELab-Zero-4B-preview-Sico-Evolution with Transformers:

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

pipe = pipeline("image-text-to-text", model="microsoft/GELab-Zero-4B-preview-Sico-Evolution")
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("microsoft/GELab-Zero-4B-preview-Sico-Evolution")
model = AutoModelForMultimodalLM.from_pretrained("microsoft/GELab-Zero-4B-preview-Sico-Evolution")
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 microsoft/GELab-Zero-4B-preview-Sico-Evolution with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/GELab-Zero-4B-preview-Sico-Evolution"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/GELab-Zero-4B-preview-Sico-Evolution",
		"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/microsoft/GELab-Zero-4B-preview-Sico-Evolution

SGLang

How to use microsoft/GELab-Zero-4B-preview-Sico-Evolution 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 "microsoft/GELab-Zero-4B-preview-Sico-Evolution" \
    --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": "microsoft/GELab-Zero-4B-preview-Sico-Evolution",
		"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 "microsoft/GELab-Zero-4B-preview-Sico-Evolution" \
        --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": "microsoft/GELab-Zero-4B-preview-Sico-Evolution",
		"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 microsoft/GELab-Zero-4B-preview-Sico-Evolution with Docker Model Runner:
```
docker model run hf.co/microsoft/GELab-Zero-4B-preview-Sico-Evolution
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🔧 This model is part of Sico — an open-source platform for building and evolving Digital Workers, where AI agents and their human operators co-evolve through real work.

⭐ Star the Sico repository to follow new evolved models and our model evolution pipeline — this GUI agent is the first public release, with more on the way.

📄 Backed by our survey on agentic evolution and co-evolving human–AI systems.

GELab-Zero-4B-preview-Sico-Evolution

A 4B GUI agent fine-tuned (LoRA) from the open-source GELab-Zero-4B-preview base model on Microsoft Edge and Copilot UI trajectories. It is built with our general-purpose GUI model evolution pipeline — an iterative mechanism that keeps lifting an agent's real task success rate round after round, and transfers to any GUI app.

Highlights

From 39.8% to 82.9%: Sico-Evolution achieves a dominant 82.9% Task Success Rate, a massive +43.1% absolute surge over the 39.8% base-model baseline.

Outperforms Closed-Source SOTAs: It edges out top proprietary giants like gpt-5.4 (79.7%), Claude-Opus-4.6 (81.3%), and claude-opus-4.7 (82.1%).

Vastly Exceeds Open-Source Models: It crushes leading competitors including kimi-k2.6 (62.6%) and UI-Venus-1.5-30B (61.0%).