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README.md

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+---
+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)
 
-# TinyLLaMA Alpaca Unsloth Fine-Tuned
+# Model Card for PRODIGY-DOIECHI & PRODIGY-SARA
 
-This model is fine-tuned on instruction-following tasks using Unsloth and Alpaca-style prompts.
+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**: aadhithyaxll@gmail.com  
+- **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!