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.
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