llamaguard

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].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"}

Workflow 5: NeMo Guardrails integration

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

When to use vs alternatives

Use LlamaGuard when:

• Need pre-trained moderation model
• Want high accuracy (94-95%)
• Have GPU resources (7-8B model)
• Need detailed safety categories
• Building production LLM apps

Model versions:

• LlamaGuard 1 (7B): Original, 6 categories
• LlamaGuard 2 (8B): Improved, 6 categories
• LlamaGuard 3 (8B): Latest (2024), enhanced

Use alternatives instead:

• OpenAI Moderation API: Simpler, API-based, free
• Perspective API: Google's toxicity detection
• NeMo Guardrails: More comprehensive safety framework
• Constitutional AI: Training-time safety

Common issues

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

Advanced topics

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.

Hardware requirements

• GPU: NVIDIA T4/A10/A100
• VRAM:
  • FP16: 14GB (7B model)
  • INT8: 7GB (quantized)
  • INT4: 4GB (QLoRA)
• CPU: Possible but slow (10× latency)
• Throughput: 50-100 req/sec (A100)

Latency (single GPU):

• HuggingFace Transformers: 300-500ms
• vLLM: 50-100ms
• Batched (vLLM): 20-50ms per request

Resources

• HuggingFace:
  • V1: https://huggingface.co/meta-llama/LlamaGuard-7b
  • V2: https://huggingface.co/meta-llama/Meta-Llama-Guard-2-8B
  • V3: https://huggingface.co/meta-llama/Meta-Llama-Guard-3-8B
• Paper: https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/
• Integration: vLLM, Sagemaker, NeMo Guardrails
• Accuracy: 94.5% (prompts), 95.3% (responses)

Agent operating procedure

1. Check the environment. Confirm the model, checkpoint, benchmark version and evaluation harness version.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Evaluate on a small subset to verify prompts, parsing and scoring.
4. Execute the full task using the instructions and references above.
5. Validate the result. Use the benchmark's official splits and metrics; check for contamination; report variance across seeds or prompts.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Scores differ from published numbers	Check prompt format, few-shot settings and harness version before concluding.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Report evaluation settings in full; never cherry-pick prompts, seeds or subsets.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• prompt-guard: Classifies text with Meta's Prompt Guard, an 86M-parameter model loaded from HuggingFace, into BENIGN, INJECTION or JAILBREAK labels to det…
• nemo-guardrails: Adds runtime safety rails to LLM applications with NVIDIA NeMo Guardrails, configured through Colang 2.0 flows.
• serving-llms-vllm: Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching.