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
```

EMBGuard-4B / README.md

nielsr HF Staff

Add model card and metadata

1d9b61e verified about 1 month ago

2.02 kB

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
Repository: 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.

Citation

If you use EMBGuard in your research, please cite:

@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}
}