# 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/)