Manas281/scheme-recommendation-model-2

### Instruction:
Domain: [Domain Name]
Indicator: [Indicator Code and Description]
Description: [Detailed description of the work/need]

Based on the above work description, recommend appropriate government schemes.

### Response:

### Instruction:
Domain: 1. Domain: Drinking Water and Sanitation
Indicator: 1.2 Household Tap Connections
Description: Extend pipeline to the Ambedkar Colony to cover 45 households

Based on the above work description, recommend appropriate government schemes.

### Response:

{
  "infrastructure_schemes": [
    {
      "identified_need": "Extend water pipeline to Ambedkar Colony for 45 households.",
      "suggested_scheme": "Jal Jeevan Mission - Ensure tap connections for each household.",
      "justification": "This scheme directly addresses the need for tap connections in Ambedkar Colony."
    }
  ],
  "individual_schemes": [],
  "total_recommendations": 1
}

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "Manas281/scheme-recommendation-model-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Prepare prompt
prompt = """### Instruction:
Domain: 1. Domain: Drinking Water and Sanitation
Indicator: 1.2 Household Tap Connections
Description: Extend pipeline to the Ambedkar Colony to cover 45 households

Based on the above work description, recommend appropriate government schemes.

### Response:
"""

# Generate recommendation
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.pad_token_id
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "Manas281/scheme-recommendation-model-2")
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")




## 💡 Use Cases

✅ **Government Planning Automation** - Automated scheme mapping for development projects  
✅ **Rural Development Systems** - AI-powered recommendation engines for gram panchayats  
✅ **E-Governance Assistants** - Chatbots for scheme information and guidance  
✅ **Educational Tools** - Training materials for public policy and administration  
✅ **Grant Application Systems** - Automated scheme identification for funding proposals

## 🎯 Major Government Schemes Covered

### Infrastructure Schemes
- **Jal Jeevan Mission (JJM)** - Tap water connections
- **Swachh Bharat Mission - Gramin (SBM-G)** - Sanitation and waste management
- **Pradhan Mantri Gram Sadak Yojana (PMGSY)** - Rural roads
- **Saubhagya Scheme** - Household electrification
- **Integrated Child Development Services (ICDS)** - Anganwadi infrastructure
- **Samagra Shiksha** - School infrastructure
- **Common Service Centers (CSC)** - Digital infrastructure

### Individual/Household Schemes
- **Pradhan Mantri Awaas Yojana - Gramin (PMAY-G)** - Housing
- **Pradhan Mantri Ujjwala Yojana (PMUY)** - LPG connections
- **Ayushman Bharat (PM-JAY)** - Health insurance
- **PM Jan Dhan Yojana (PMJDY)** - Bank accounts
- **PM Suraksha Bima Yojana (PMSBY)** - Accident insurance
- **PM Jeevan Jyoti Bima Yojana (PMJJBY)** - Life insurance
- **National Social Assistance Programme (NSAP)** - Social pensions
- **Pre-Matric and Post-Matric Scholarships** - SC student support
- **PM Kaushal Vikas Yojana (PMKVY)** - Skill training
- **DAY-NRLM** - Self-Help Groups and livelihoods
- **Mahatma Gandhi NREGA** - Employment generation
- **Soil Health Card Scheme** - Agricultural support

## ⚠️ Limitations

1. **Dataset Size** - Trained on 179 examples; coverage may be incomplete for edge cases
2. **Geographic Focus** - Primarily focused on Indian government schemes
3. **Output Repetition** - Model may occasionally generate repeated recommendations (requires post-processing)
4. **JSON Parsing** - Some outputs may need cleaning to extract valid JSON
5. **Scheme Updates** - Does not reflect scheme changes after training cutoff date (2024)
6. **Language** - Primarily English; limited Hindi understanding

## 🔮 Future Improvements

- [ ] Expand dataset to 500+ examples covering more states and districts
- [ ] Add scheme eligibility criteria and application procedures
- [ ] Include budget allocation recommendations
- [ ] Multi-language support (Hindi, regional languages)
- [ ] Integration with live scheme databases for real-time updates
- [ ] Retrieval-Augmented Generation (RAG) for hybrid recommendations
- [ ] State-specific scheme variants and customizations
- [ ] Mobile-optimized version for field workers

## 🛠️ Technical Stack

- **Framework:** Hugging Face Transformers + PEFT
- **Base Model:** Microsoft Phi-3-mini-4k-instruct
- **Training:** LoRA (Low-Rank Adaptation)
- **Quantization:** 8-bit during training
- **Hardware:** GPU (CUDA-enabled)
- **Languages:** Python

## 📄 Citation

If you use this model in your research or applications, please cite:

## 👨‍💻 Author

**Manas Patil**  
🔗 [Hugging Face Profile](https://huggingface.co/Manas281)  


## 📜 License

This model is released under the **Apache 2.0 License**, same as the base Phi-3 model.

## 🙏 Acknowledgments

- Microsoft for the Phi-3-mini-4k-instruct base model
- Hugging Face for the Transformers and PEFT libraries
- The open-source AI community for tools and resources

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**Model Card Version:** 1.0  
**Last Updated:** January 2025  
**Status:** Production-ready for testing and evaluation