llamaguardlisted

# 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.gener