Instructions to use EMBGuard/EMBGuard-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EMBGuard/EMBGuard-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="EMBGuard/EMBGuard-4B")
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("EMBGuard/EMBGuard-4B")
model = AutoModelForMultimodalLM.from_pretrained("EMBGuard/EMBGuard-4B")
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 EMBGuard/EMBGuard-4B with vLLM:

Install from pip and serve model

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

SGLang

How to use EMBGuard/EMBGuard-4B 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 "EMBGuard/EMBGuard-4B" \
    --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": "EMBGuard/EMBGuard-4B",
		"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 "EMBGuard/EMBGuard-4B" \
        --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": "EMBGuard/EMBGuard-4B",
		"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 EMBGuard/EMBGuard-4B with Docker Model Runner:
```
docker model run hf.co/EMBGuard/EMBGuard-4B
```

Add model card and metadata

by nielsr HF Staff - opened 25 days ago

base: refs/heads/main

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from: refs/pr/1

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+---
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model: Qwen/Qwen3-4B-Instruct-2507
+---
+# EMBGuard
+EMBGuard is the first MLLM-based safety guardrail for embodied agents designed to decouple physical risk reasoning from agent policy. By evaluating a (visual observation, action) pair, EMBGuard identifies hazardous configurations and provides natural language explanations of potential risks.
+- **Paper:** [EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents](https://huggingface.co/papers/2605.30924)
+- **Repository:** [https://github.com/dongwxxkchoi/EMBGuard](https://github.com/dongwxxkchoi/EMBGuard)
+## Model Description
+MLLM-powered embodied agents deployed in real-world environments encounter physical hazards. However, existing approaches lack explicit mechanisms for identifying hazards and reasoning about action-conditioned risks, leading agents to either miss risky interactions or over-identify risks.
+EMBGuard identifies hazardous configurations across seven physical risk categories and provides natural language explanations of potential risks. Despite its compact size, EMBGuard achieves performance competitive with proprietary MLLMs (e.g., GPT-5.1, Gemini-2.5-Pro) while significantly reducing false-positive rates, making it suitable for real-time deployment in safety-critical planning.
+The model was trained on **EMBHazard**, a dataset of 15.1K action-conditioned pairs, and evaluated on **EMBGuardTest**, a benchmark of 329 manually curated real-world scenarios.
+## Usage
+For detailed instructions on installation, training, and evaluation, please refer to the [official GitHub repository](https://github.com/dongwxxkchoi/EMBGuard).
+## Citation
+If you use EMBGuard in your research, please cite:
+```bibtex
+@article{choi2024embguard,
+  title={EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents},
+  author={Choi, Dongwook and others},
+  journal={arXiv preprint arXiv:2605.30924},
+  year={2024}
+}
+```