README.md

---
language: [en]                     # list of supported languages
license: apache-2.0                # license type (e.g. mit, apache-2.0)
tags:
- llm
- classification
- religion-prediction
datasets:
- custom                          # datasets used for training
model-index:
- name: onomastics
  results:
  - task:
      type: text-classification    # e.g. text-generation, text-classification
      name: Religion Classification
    dataset:
      name: custom-dataset
      type: text
    metrics:
    - name: accuracy
      type: accuracy
      value: 0.92                  # put your evaluation results here
---

# 🧾 Model Card: your-model-name

## 📌 Model Details
- **Developed by:** gpsworld8800  
- **Model type:** Large Language Model  
- **Architecture:** LLaMA  
- **Language(s):** English / Hindi / Multilingual  
- **License:** Apache 2.0  

## 🛠️ Intended Uses
- Predicting religion/ethnicity from Indian names  
- Research on Indian onomastics & linguistics  
- Educational or demo purposes  

⚠️ **Not intended for**:  
- Making decisions in sensitive contexts (hiring, loans, etc.)  
- Any discriminatory use  

## 🧑‍💻 How to Use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gpsworld8800/onomastics")
model = AutoModelForCausalLM.from_pretrained("gpsworld8800/onomastics")

inputs = tokenizer("Prathamesh Gate", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))