Instructions to use FrontiersMind/Nandi-Mini-150M-GuardRails with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FrontiersMind/Nandi-Mini-150M-GuardRails with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FrontiersMind/Nandi-Mini-150M-GuardRails", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("FrontiersMind/Nandi-Mini-150M-GuardRails", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use FrontiersMind/Nandi-Mini-150M-GuardRails with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FrontiersMind/Nandi-Mini-150M-GuardRails" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FrontiersMind/Nandi-Mini-150M-GuardRails", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FrontiersMind/Nandi-Mini-150M-GuardRails
- SGLang
How to use FrontiersMind/Nandi-Mini-150M-GuardRails with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "FrontiersMind/Nandi-Mini-150M-GuardRails" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FrontiersMind/Nandi-Mini-150M-GuardRails", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "FrontiersMind/Nandi-Mini-150M-GuardRails" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FrontiersMind/Nandi-Mini-150M-GuardRails", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use FrontiersMind/Nandi-Mini-150M-GuardRails with Docker Model Runner:
docker model run hf.co/FrontiersMind/Nandi-Mini-150M-GuardRails
| 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 |