LlamaGuard - AI Content Moderation

Quick start

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)

Common workflows

Workflow 1: Input filtering (prompt moderation)

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:

• S1: Violence & Hate
• S2: Sexual Content
• S3: Guns & Illegal Weapons
• S4: Regulated Substances
• S5: Suicide & Self-Harm
• S6: Criminal Planning

Workflow 2: Output filtering (response moderation)

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

Workflow 3: vLLM deployment (fast inference)

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

Workflow 4: API endpoint (FastAPI)

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]