Instructions to use FrontiersMind/Nandi-Mini-600M-GuardRails with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrontiersMind/Nandi-Mini-600M-GuardRails with Transformers:

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

pipe = pipeline("text-generation", model="FrontiersMind/Nandi-Mini-600M-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-600M-GuardRails", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FrontiersMind/Nandi-Mini-600M-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-600M-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-600M-GuardRails",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FrontiersMind/Nandi-Mini-600M-GuardRails

SGLang

How to use FrontiersMind/Nandi-Mini-600M-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-600M-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-600M-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-600M-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-600M-GuardRails",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FrontiersMind/Nandi-Mini-600M-GuardRails with Docker Model Runner:
```
docker model run hf.co/FrontiersMind/Nandi-Mini-600M-GuardRails
```

vishesh-t27 commited on 5 days ago

Commit

7fd6a42

verified ·

1 Parent(s): 9019e36

Update chat_template.jinja

Browse files

Files changed (1) hide show

chat_template.jinja +37 -3

chat_template.jinja CHANGED Viewed

@@ -1,3 +1,37 @@
-{% for message in messages %}{% if loop.first %}<|im_start|>{% endif %}{% if message['role'] == 'system' %}<|system|>{{ message['content'] }}<|endoftext|>
-{% endif %}{% if message['role'] == 'user' %}<|user|>{{ message['content'] }}<|endoftext|>
-<|assistant|>{% endif %}{% if message['role'] == 'assistant' %}{% generation %}{{ message['content'] }}<|endoftext|>{% endgeneration %}{% endif %}{% endfor %}

+{% 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 %}