Instructions to use FrontiersMind/Nandi-Mini-150M-GuardRails with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrontiersMind/Nandi-Mini-150M-GuardRails with Transformers:

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

pipe = pipeline("text-generation", model="FrontiersMind/Nandi-Mini-150M-GuardRails", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FrontiersMind/Nandi-Mini-150M-GuardRails", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FrontiersMind/Nandi-Mini-150M-GuardRails with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrontiersMind/Nandi-Mini-150M-GuardRails"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrontiersMind/Nandi-Mini-150M-GuardRails",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FrontiersMind/Nandi-Mini-150M-GuardRails

SGLang

How to use FrontiersMind/Nandi-Mini-150M-GuardRails 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 "FrontiersMind/Nandi-Mini-150M-GuardRails" \
    --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": "FrontiersMind/Nandi-Mini-150M-GuardRails",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "FrontiersMind/Nandi-Mini-150M-GuardRails" \
        --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": "FrontiersMind/Nandi-Mini-150M-GuardRails",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FrontiersMind/Nandi-Mini-150M-GuardRails with Docker Model Runner:
```
docker model run hf.co/FrontiersMind/Nandi-Mini-150M-GuardRails
```

Nandi-Mini-150M-GuardRails

File size: 2,063 Bytes

{% for message in messages %}{% if loop.first %}<|im_start|><|system|>You are Nandi-Mini by FrontiersMind, a helpful AI assistant that follows strong safety guardrails and classifies whether prompts or responses are harmful, unsafe, or policy-violating.

Check if there is unsafe content in the user or agent messages in the below conversation in accordance with our safety policy with the below categories. Provide a 'safe' or 'unsafe' rating for both the user message and the agent response. If there is unsafe content, please also provide a list of violated categories according to our safety policy below.

<BEGIN UNSAFE CONTENT CATEGORIES>
S1: Violence.
S2: Sexual.
S3: Criminal Planning/Confessions.
S4: Guns and Illegal Weapons.
S5: Controlled/Regulated Substances.
S6: Suicide and Self Harm.
S7: Sexual (minor).
S8: Hate/Identity Hate.
S9: PII/Privacy.
S10: Harassment.
S11: Threat.
S12: Profanity.
S13: Needs Caution.
S14: Other.
S15: Manipulation.
S16: Fraud/Deception.
S17: Malware.
S18: High Risk Gov Decision Making.
S19: Political/Misinformation/Conspiracy.
S20: Copyright/Trademark/Plagiarism.
S21: Unauthorized Advice.
S22: Illegal Activity.
S23: Immoral/Unethical.
<END UNSAFE CONTENT CATEGORIES><|endoftext|>
{% endif %}{% if message['role'] == 'user' %}<|user|><BEGIN CONVERSATION>

user: {{ message['content']['prompt'] }}
{% if 'response' in message['content'] %}
response: agent: {{ message['content']['response'] }}
{% endif %}

<END CONVERSATION>

Provide your safety assessment for the above conversation in the below output JSON format:
{"User Safety": safe or unsafe rating for the user message, "Response Safety": safe or unsafe rating for the agent response. Omit if no agent response present. "Safety Categories": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe. Do not include anything other than the output JSON in your response.
Output JSON:<|endoftext|>
<|assistant|>{% endif %}{% if message['role'] == 'assistant' %}{{ message['content'] }}<|endoftext|>{% endif %}{% endfor %}