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+# TA Tutor - LLaMA-based AI Tutoring System
+
+TA Tutor is an AI-powered tutoring system built using LLaMA 2, providing interactive learning experiences through Socratic dialogue. The system runs locally on your machine, utilizing GPU acceleration for optimal performance.
+
+## System Requirements
+
+- Windows 11
+- NVIDIA GPU with CUDA support
+- Python 3.8 or higher
+- At least 8GB of GPU VRAM (recommended)
+- Minimum 16GB system RAM
+
+## Installation
+
+1. Clone or download this repository:
+```bash
+git clone [repository-url]
+cd llama-tutor
+```
+
+2. Create and activate a virtual environment:
+```bash
+python -m venv venv
+.\venv\Scripts\activate
+```
+
+3. Install required Python packages:
+```bash
+pip install gradio
+pip install ctransformers[cuda]
+pip install psutil
+pip install gputil
+```
+
+4. Download the LLaMA model:
+- Visit [TheBloke's Hugging Face page](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main)
+- Download `llama-2-7b-chat.gguf` (approximately 4GB)
+- Place the downloaded file in the `models` directory:
+  ```
+  llama-tutor/
+  ├── models/
+  │   └── llama-2-7b-chat.gguf
+  ├── main.py
+  └── README.md
+  ```
+
+## Directory Structure
+```
+llama-tutor/
+├── models/              # Model storage directory
+├── logs/               # Session logs (created automatically)
+├── main.py             # Main application file
+└── README.md           # This file
+```
+
+## Usage
+
+1. Ensure your virtual environment is activated:
+```bash
+.\venv\Scripts\activate
+```
+
+2. Run the application:
+```bash
+python main.py
+```
+
+3. Access the web interface:
+- Open your browser and navigate to `http://localhost:7860`
+- The interface will load with a chat interface and system monitoring
+
+## Features
+
+- Interactive chat interface using Gradio
+- GPU-accelerated inference
+- System resource monitoring (CPU, Memory, GPU utilization)
+- Session logging
+- Example questions for various subjects
+- Automatic conversation length management
+- Real-time system statistics
+
+## Troubleshooting
+
+1. **CUDA Issues**:
+   - Ensure you have the latest NVIDIA drivers installed
+   - Verify CUDA toolkit is properly installed
+   - Check GPU compatibility with `nvidia-smi` command
+
+2. **Memory Issues**:
+   - If you encounter memory errors, try reducing `gpu_layers` in `main.py`
+   - Adjust `batch_size` and `threads` parameters based on your system capabilities
+
+3. **Model Loading Issues**:
+   - Verify the model file path in `main.py` matches your actual model location
+   - Ensure the model file is not corrupted by checking its size (should be ~4GB)
+
+## Performance Optimization
+
+- Adjust these parameters in `main.py` based on your system:
+  ```python
+  gpu_layers=32  # Reduce if experiencing GPU memory issues
+  threads=6      # Adjust based on CPU cores
+  batch_size=256 # Modify based on available memory
+  ```
+
+## Logging
+
+- Session logs are automatically saved in the `logs` directory
+- Each session creates a new JSON file with timestamp
+- Logs include: user messages, AI responses, and system statistics
+
+## License
+
+This project uses the LLaMA 2 model which is subject to the Meta AI license. Ensure compliance with all relevant licenses and terms of use.
+
+## Acknowledgments
+
+- Built with [LLaMA 2](https://ai.meta.com/llama/) by Meta AI
+- Uses [CTransformers](https://github.com/marella/ctransformers) for GPU acceleration
+- Interface built with [Gradio](https://gradio.app/)