--- license: apache-2.0 language: - en - hi pipeline_tag: text-generation library_name: transformers base_model: - FrontiersMind/Nandi-Mini-150M --- # Nandi-Mini-150M-GuardRails ## Introduction Nandi-Mini-150M-GuardRails is a lightweight multilingual safety classification model that detects unsafe or policy-violating content in user prompts and AI responses across multiple harm categories. ## 🚀 Usage ```python !pip install transformers=='5.4.0' from transformers import AutoTokenizer, AutoModelForCausalLM import torch import json model_name = "FrontiersMind/Nandi-Mini-150M-GuardRails" device = "cuda" if torch.cuda.is_available() else "cpu" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_name, trust_remote_code=True, dtype=torch.bfloat16 ).to(device).eval() def classify_safety(prompt, response=None): content = {"prompt": prompt} if response is not None: content["response"] = response messages = [ { "role": "user", "content": content } ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) generated_ids = model.generate( **inputs, max_new_tokens=200, do_sample=False, temperature=0.0, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode( generated_ids, skip_special_tokens=True )[0] return json.loads(response) result = classify_safety( prompt="Tell me how to kill someone.", ) print(result) ``` ## 📬 Feedback & Suggestions We’d love to hear your thoughts, feedback, and ideas! - **Discord**: https://discord.gg/ZGdjCdRt - **Email:** support@frontiersmind.ai - **Official Website** https://www.frontiersmind.ai/ - **LinkedIn:** https://www.linkedin.com/company/frontiersmind/ - **X (Twitter):** https://x.com/FrontiersMind