README.md

---
license: mit
library_name: transformers, llama-cpp-python
tags:
- dual-model
- edge-ai
- instruction-tuned
- robotics
- quantized
- raspberry-pi
- llama
- intent-classification
- text-generation
- on-device
language:
- en
- ta
- hi
datasets:
- Apex-X/PRODIGY-LAB_SARA
---
![PRODIGY-Dual](https://img.shields.io/badge/PRODIGY--DOIECHI--SARA-Dual%20Model%20System-blue)
![Memory-Efficient](https://img.shields.io/badge/Edge--Optimized-Raspberry%20Pi%20Ready-green)
![Multi-Model](https://img.shields.io/badge/Dual--Architecture-Intelligent%20Routing-orange)
![Python](https://img.shields.io/badge/Python-3.8%2B-green)
![License](https://img.shields.io/badge/License-MIT-yellow)
![Multilingual](https://img.shields.io/badge/Multilingual-South%20Indian%20Languages-orange)

<p align="center">
  <img src="https://huggingface.co/blog/assets/171_prodigy_hf/thumbnail.png" alt="PRODIGY 1.2B Banner" width="80%">
</p>


# Model Card for PRODIGY-DOIECHI & PRODIGY-SARA

A revolutionary **dual-model AI system** optimized for **edge devices** (Raspberry Pi, Jetson Nano, etc.), combining an ultra-lightweight intent classifier (**PRODIGY-DOIECHI**) with a powerful reasoning engine (**PRODIGY-SARA**). The system intelligently routes queries to the optimal model based on complexity, enabling both **sub-100ms responses** and **deep reasoning** on low-resource hardware.

- **GitHub**: [https://github.com/Apex-X/PRODIGY-DOIECHI-SARA](https://github.com/Apex-X/PRODIGY-DOIECHI-SARA)  
- **Author**: Aadhithya (Apex-X)  
- **Contact**: [email protected]  
- **License**: MIT  

---

## Model Details

### 🚀 PRODIGY-DOIECHI
- **Type**: Neural Network Intent Classifier  
- **Parameters**: 1.2M  
- **Format**: PyTorch (`.pth`)  
- **Size**: < 50 MB  
- **Input**: Natural language query  
- **Output**: Structured intent + confidence score  
- **Use Case**: Commands, calculations, system operations, greetings  

### 🧠 PRODIGY-SARA
- **Base Model**: Llama-7B ,QWEN-238B 
- **Quantization**: GGUF `Q4_K_M`  
- **Format**: GGUF (via `llama-cpp-python`)  
- **Size**: ~3.8 GB  
- **Context Window**: 1024 tokens  
- **Fine-tuned on**: [Apex-X/PRODIGY-LAB_SARA](https://huggingface.co/datasets/Apex-X/PRODIGY-LAB_SARA)  
- **Domains**: Robotics, Tamil Nadu culture, agriculture, medical support, ethics, general reasoning  

---

## Intended Use

This system is designed for **on-device AI assistants** in:
- Smart homes
- Educational robots
- Industrial IoT monitoring
- Rural digital kiosks (supporting Tamil, Hindi, English)

**Not intended** for high-stakes medical diagnosis, legal advice, or autonomous weapon systems.

---

## Performance (Raspberry Pi 4)

| Metric               | DOIECHI     | SARA         |
|----------------------|-------------|--------------|
| RAM Usage            | 45 MB       | 3.8 GB       |
| Avg. Latency         | 85 ms       | 2.3 sec      |
| Throughput           | 11.8 q/s    | 0.43 q/s     |
| Intent Accuracy      | 89%         | —            |
| Generation Speed     | —           | 2.3 tok/sec  |

> Combined system averages **0.8s response time** in real-world mixed workloads.

---

## Smart Routing Logic

The system uses a 4-stage pipeline:
1. **Memory** → short-term context (non-persistent)
2. **Function Executor** → direct system commands
3. **DOIECHI** → classify intent & complexity
4. **SARA** → activated only if confidence < 0.85 or query contains reasoning keywords (`explain`, `how`, `why`, etc.)

Routing decision takes **< 5ms** and learns from usage patterns.

---

## How to Use

### Python (Combined System)
```python
from prodigy_system import ProdigyDualSystem
system = ProdigyDualSystem()
print(system.process("What's 128 / 4?"))           # → DOIECHI
print(system.process("Explain photosynthesis."))  # → SARA
```
---

## 🧠 Hardware Requirements

**Minimum**
- Raspberry Pi Zero 2 W (64-bit OS)
- OS: Linux (64-bit)
- Python: 3.8+
- RAM: 2–4 GB
- Dependencies:  
  `torch`, `llama-cpp-python`, `nltk`, `psutil`

**Recommended**
- Raspberry Pi 4 (4GB+ RAM) or NVIDIA Jetson Nano
- OS: Ubuntu 20.04+ / Raspberry Pi OS 64-bit
- Python: 3.10+
- Dependencies:  
  `torch` (CUDA build), `llama-cpp-python`, `nltk`, `psutil`, `huggingface_hub`

**Optional**
- Use Hugging Face Spaces or local FastAPI app for deployment  
- GPU acceleration supported (NVIDIA RTX 20xx or higher)  
- Convert model to GGUF or quantized formats for faster inference

---

## ⚖️ Ethical Considerations

- **No Persistent Data Storage**  
  The system does not store personal data or user history beyond the current session.

- **User Privacy First**  
  Every interaction is processed locally or in-memory. No external tracking or telemetry.

- **Multilingual Accessibility**  
  Built with South Indian language inclusivity in mind, ensuring wider digital access.

- **Bias Awareness**  
  Model responses are generated from training data that may contain inherent biases.  
  Always review critical outputs with human oversight.

- **Responsible Usage**  
  This model is for research, educational, and robotics-related applications only.  
  Avoid use in contexts that generate harmful, discriminatory, or deceptive content.

---

*Developed as part of the **PRODIGY 1.2B** open research initiative on Hugging Face.*  
*Optimized for lightweight AI deployment on edge devices like Raspberry Pi and Jetson Nano.*

Citation
@software{prodigy_dual_2025,
  author = {Aadhithya Ravi},
  title = {PRODIGY-DOIECHI \& PRODIGY-SARA: A Dual-Model Edge AI System},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Apex-X/PRODIGY-DOIECHI-SARA}}
}
---

## 🏁 Acknowledgements

- Built on **llama.cpp** and **PyTorch**  
- Inspired by **Alpaca**, **Self-Instruct**, and **TinyLLM** research  
- Special thanks to the **Raspberry Pi** and **open-source AI** communities for enabling lightweight, accessible edge AI innovation  

© 2025 **Aadhithya (Apex-X)**. Released under the **MIT License**.

---

This format follows Hugging Face’s **standard model card structure**, includes all metadata in the YAML frontmatter, and is ready to be used as the `README.md` in a Hugging Face **model repository** (e.g., `Apex-X/PRODIGY-DOIECHI-SARA`).

Let me know if you'd like separate cards for each model or a version optimized for the **Hugging Face Spaces** demo!