LLM-From-Scratch

Welcome to the LLM-From-Scratch repository! This project is a comprehensive guide to understanding and implementing a Large Language Model (LLM) architecture step by step. By diving into this repository, you'll learn the fundamentals of Transformer-based architectures, the building blocks of LLMs, and how to train them effectively using Python and PyTorch.

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Project Overview

Large Language Models (LLMs) have transformed the field of Natural Language Processing (NLP). This project provides a didactic approach to creating an LLM from scratch, covering:

• Preprocessing text data.
• Building embedding layers.
• Implementing multi-head attention and encoder layers.
• Training the architecture with a small dataset.

While the model built here is not production-grade, it serves as a powerful learning tool for anyone interested in understanding the inner workings of LLMs.

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Key Features

1. Hands-on Learning: Implement each module of the Transformer architecture from scratch.
2. Comprehensive Explanation: All steps are well-documented with detailed comments and markdown cells in the Jupyter Notebook.
3. Dataset Included: A toy dataset (text.txt) is provided to simplify the training process and allow quick experimentation.
4. End-to-End Process: From data preprocessing to training and evaluating the model, all steps are included.

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Getting Started

Prerequisites

Make sure you have the following installed:

• Python 3.8 or later
• PyTorch
• NumPy
• A Jupyter Notebook environment

You can install all required packages with:

pip install -r requirements.txt

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Installation

Clone this repository to your local machine:

git clone https://github.com/Anello92/LLM-From-Scratch.git
cd LLM-From-Scratch

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Project Structure

The repository is structured as follows:

LLM-From-Scratch/
├── LLM_From_Scratch.ipynb  # Main Jupyter Notebook for the project
├── text.txt                # Dataset used for training
├── README.md               # Repository documentation
└── requirements.txt        # List of dependencies

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How to Use

1. Run the Notebook
   Open LLM_From_Scratch.ipynb in Jupyter Notebook or JupyterLab and follow the steps. Each cell is well-documented to guide you through the process.

2. Experiment with the Dataset
   Modify the text.txt dataset to observe how the model adapts to new input data.

3. Learn and Extend
   Use this repository as a foundation to explore advanced topics like fine-tuning, large-scale training, or integrating pre-trained models.

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Results and Insights

• Model Training: The provided implementation demonstrates how an LLM architecture learns patterns in a small dataset.
• Limitations: Due to the limited data size and computational resources, the model is not optimized for high-performance tasks but provides a solid understanding of LLM design principles.

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Contributing

Contributions are welcome! Feel free to submit a pull request or open an issue to discuss improvements or ideas.

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Acknowledgments

• PyTorch Team: For providing an exceptional framework for deep learning.
• Hugging Face: For inspiring the NLP community with pre-trained models.
• BERT Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018).

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