🧠 PyTorch Language Model

📜 Overview

This project implements a neural language model using PyTorch. It's designed to generate text based on patterns learned from input data. Perfect for natural language processing enthusiasts and AI researchers! 🚀

✨ Features

• 🔤 Character-level language modeling
• 🏗️ Transformer architecture with multi-head attention
• 🔄 Train on custom text datasets
• 🎭 Generate new text based on learned patterns
• 🖥️ GPU acceleration support

🛠️ Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.6 or higher
• PyTorch
• CUDA-capable GPU (optional, but recommended for faster training)

🔧 Installation

1. Clone this repository:

   git clone https://github.com/yourusername/pytorch-language-model.git
   cd pytorch-language-model
   

2. Install the required packages:

   pip install torch
   

📁 Project Structure

• main.py: The main script containing the model architecture and training loop
• input.txt: Your input text file for training the model

🚀 Usage

1. Prepare your input data:

   • Place your text data in a file named input.txt in the project directory.

2. Run the main script:

   python main.py
   

3. The script will start training the model on your input data. You'll see loss values printed at regular intervals.

4. After training, you can generate text by uncommenting the generation code at the end of the script.

🎛️ Customization

You can adjust the following hyperparameters in the script:

• batch_size: Number of sequences processed in parallel
• block_size: Maximum context length for predictions
• max_iters: Number of training iterations
• learning_rate: Step size for optimizer
• n_embd: Embedding dimension
• n_head: Number of attention heads
• n_layer: Number of transformer blocks

📊 Model Architecture

The model uses a transformer-based architecture with the following components:

• 🔢 Token and position embeddings
• 🤯 Multi-head self-attention mechanism
• 🔀 Feed-forward neural networks
• 📊 Layer normalization

⚠️ Notes

• Training time can be significant for large datasets or models.
• GPU acceleration is recommended for faster training.
• Generated text quality improves with more training data and iterations.

🤝 Contributing

Contributions to this Language Model project are welcome! Feel free to submit a Pull Request. 🎉

🙏 Acknowledgements

• PyTorch team for the amazing deep learning framework
• Attention is All You Need paper for the transformer architecture