llamaguard

# LlamaGuard - AI Content Moderation ## Quick start LlamaGuard is a 7-8B parameter model specialized for content safety classification. **Installation**: ```bash pip install transformers torch # Login to HuggingFace (required) huggingface-cli login ``` **Basic usage**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```python 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**: ```bash 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**: ```python 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: ```bash 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: ```python from vllm import LLM llm = LLM(model="meta-llama/LlamaGuard-7b") # Latency: 500ms → 50ms ``` Enable tensor parallelism: ```python llm = LLM(model="meta-llama/LlamaGuard-7b", tensor_parallel_size=2) # 2× faster on 2 GPUs ``` **Issue: False positives** Use threshold-based filtering: ```python # 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: ```python 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](references/custom-categories.md) for fine-tuning LlamaGuard with domain-specific safety categories. **Performance benchmarks**: See [references/benchmarks.md](references/benchmarks.md) for accuracy comparison with other moderation APIs and latency optimization. **Deployment guide**: See [references/deployment.md](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) --- > Converted and distributed by [TomeVault](https://tomevault.io) | [Claim this content](https://tomevault.io/claim/davila7/claude-code-templates) <!-- tomevault:2.0:skill_md:2026-04-05 -->