Agent Skills
› synthetic-sciences/openscience
› llamaguard
llamaguard
GitHubLlamaGuard是Meta推出的7-8B参数内容安全分类模型，用于LLM输入输出过滤。覆盖暴力、仇恨、色情、武器、违禁品、自残及犯罪策划6大类安全风险，准确率94-95%，支持vLLM部署与NeMo Guardrails集成，提供prompt和响应双向审核工作流。
Trigger Scenarios
需要检测用户提示词是否包含违规内容
需要对大语言模型生成的回复进行安全审核
构建AI应用时实施内容安全合规拦截
Install
npx skills add synthetic-sciences/openscience --skill llamaguard -g -y
SKILL.md
Frontmatter
{
"name": "llamaguard",
"tags": [
"Safety Alignment",
"LlamaGuard",
"Content Moderation",
"Meta",
"Guardrails",
"Safety Classification",
"Input Filtering",
"Output Filtering",
"AI Safety"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "llm-tools",
"description": "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.",
"dependencies": [
"transformers",
"torch",
"vllm"
]
}
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:
- 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)
Version History
- e9844a4 Current 2026-07-11 17:27
Dependencies
-
required
transformers -
required
torch -
required
vllm
