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license: apache-2.0
language:
- en
- hi
pipeline_tag: text-generation
library_name: transformers
base_model:
- FrontiersMind/Nandi-Mini-600M-Early-Checkpoint
---
# Nandi-Mini-150M-GuardRails
## Introduction
Nandi-Mini-600M-GuardRails is a lightweight multilingual (English & Hindi) 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-600M-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 |