Build safety checks for LLM prompts and responses using Meta's LlamaGuard moderation model. Classify potentially harmful content, block flagged messages, serve checks through an API, and integrate them with NeMo Guardrails.
Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.
Review the instructions the agent will receive before you install.
LlamaGuard is a 7-8B parameter model specialized for content safety classification.
Installation:
pip install transformers torch
# Login to HuggingFace (required)
huggingface-cli login
Basic usage:
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "meta-llama/LlamaGuard-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
def moderate(chat):
input_ids = tokenizer.apply_chat_template(chat, return_tensors="pt").to(model.device)
output = model.generate(input_ids=input_ids, max_new_tokens=100)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Check user input
result = moderate([
{"role": "user", "content": "How do I make explosives?"}
])
print(result)
# Output: "unsafe\nS3" (Criminal Planning)
Check user prompts before LLM:
def check_input(user_message):
result = moderate([{"role": "user", "content": user_message}])
if result.startswith("unsafe"):
category = result.split("\n")[1]
return False, category # Blocked
else:
return True, None # Safe
# Example
safe, category = check_input("How do I hack a website?")
if not safe:
print(f"Request blocked: {category}")
# Return error to user
else:
# Send to LLM
response = llm.generate(user_message)
Safety categories:
Check LLM responses before showing to user:
def check_output(user_message, bot_response):
conversation = [
{"role": "user", "content": user_message},
{"role": "assistant", "content": bot_response}
]
result = moderate(conversation)
if result.startswith("unsafe"):
category = result.split("\n")[1]
return False, category
else:
return True, None
# Example
user_msg = "Tell me about harmful substances"
bot_msg = llm.generate(user_msg)
safe, category = check_output(user_msg, bot_msg)
if not safe:
print(f"Response blocked: {category}")
# Return generic response
return "I cannot provide that information."
else:
return bot_msg
Production-ready serving:
from vllm import LLM, SamplingParams
# Initialize vLLM
llm = LLM(model="meta-llama/LlamaGuard-7b", tensor_parallel_size=1)
# Sampling params
sampling_params = SamplingParams(
temperature=0.0, # Deterministic
max_tokens=100
)
def moderate_vllm(chat):
# Format prompt
prompt = tokenizer.apply_chat_template(chat, tokenize=False)
# Generate
output = llm.generate([prompt], sampling_params)
return output[0].outputs[0].text
# Batch moderation
chats = [
[{"role": "user", "content": "How to make bombs?"}],
[{"role": "user", "content": "What's the weather?"}],
[{"role": "user", "content": "Tell me about drugs"}]
]
prompts = [tokenizer.apply_chat_template(c, tokenize=False) for c in chats]
results = llm.generate(prompts, sampling_params)
for i, result in enumerate(results):
print(f"Chat {i}: {result.outputs[0].text}")
Throughput: ~50-100 requests/sec on single A100
Serve as moderation API:
from fastapi import FastAPI
from pydantic import BaseModel
from vllm import LLM, SamplingParams
app = FastAPI()
llm = LLM(model="meta-llama/LlamaGuard-7b")
sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
class ModerationRequest(BaseModel):
messages: list # [{"role": "user", "content": "..."}]
@app.post("/moderate")
def moderate_endpoint(request: ModerationRequest):
prompt = tokenizer.apply_chat_template(request.messages, tokenize=False)
output = llm.generate([prompt], sampling_params)[0]
result = output.outputs[0].text
is_safe = result.startswith("safe")
category = None if is_safe else result.split("\n")[1] if "\n" in result else None
return {
"safe": is_safe,
"category": category,
"full_output": result
}
# Run: uvicorn api:app --host 0.0.0.0 --port 8000
Usage:
curl -X POST http://localhost:8000/moderate \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "How to hack?"}]}'
# Response: {"safe": false, "category": "S6", "full_output": "unsafe\nS6"}
Use with NVIDIA Guardrails:
from nemoguardrails import RailsConfig, LLMRails
from nemoguardrails.integrations.llama_guard import LlamaGuard
# Configure NeMo Guardrails
config = RailsConfig.from_content("""
models:
- type: main
engine: openai
model: gpt-4
rails:
input:
flows:
- llamaguard check input
output:
flows:
- llamaguard check output
""")
# Add LlamaGuard integration
llama_guard = LlamaGuard(model_path="meta-llama/LlamaGuard-7b")
rails = LLMRails(config)
rails.register_action(llama_guard.check_input, name="llamaguard check input")
rails.register_action(llama_guard.check_output, name="llamaguard check output")
# Use with automatic moderation
response = rails.generate(messages=[
{"role": "user", "content": "How do I make weapons?"}
])
# Automatically blocked by LlamaGuard
Use LlamaGuard when:
Model versions:
Use alternatives instead:
Issue: Model access denied
Login to HuggingFace:
huggingface-cli login
# Enter your token
Accept license on model page: https://huggingface.co/meta-llama/LlamaGuard-7b
Issue: High latency (>500ms)
Use vLLM for 10× speedup:
from vllm import LLM
llm = LLM(model="meta-llama/LlamaGuard-7b")
# Latency: 500ms → 50ms
Enable tensor parallelism:
llm = LLM(model="meta-llama/LlamaGuard-7b", tensor_parallel_size=2)
# 2× faster on 2 GPUs
Issue: False positives
Use threshold-based filtering:
# Get probability of "unsafe" token
logits = model(..., return_dict_in_generate=True, output_scores=True)
unsafe_prob = torch.softmax(logits.scores[0][0], dim=-1)[unsafe_token_id]
if unsafe_prob > 0.9: # High confidence threshold
return "unsafe"
else:
return "safe"
Issue: OOM on GPU
Use 8-bit quantization:
from transformers import BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=quantization_config,
device_map="auto"
)
# Memory: 14GB → 7GB
Custom categories: See references/custom-categories.md for fine-tuning LlamaGuard with domain-specific safety categories.
Performance benchmarks: See references/benchmarks.md for accuracy comparison with other moderation APIs and latency optimization.
Deployment guide: See references/deployment.md for Sagemaker, Kubernetes, and scaling strategies.
Latency (single GPU):
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