🤖 Transformers From Scratch (PyTorch)

A minimal implementation of the Transformer architecture from scratch using PyTorch.

This project demonstrates the core concepts introduced in the groundbreaking paper:

Attention Is All You Need

The goal of this repository is to understand and implement the internal mechanics of transformers including attention, positional encoding, and encoder-decoder architecture.

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🧠 What is a Transformer?

Transformers are deep learning architectures designed to handle sequential data using attention mechanisms instead of recurrence or convolution.

They are widely used in:

• Natural Language Processing
• Large Language Models
• Vision Transformers
• Speech Recognition
• Multimodal AI

Popular models based on transformers include:

• GPT
• BERT
• T5
• Vision Transformers

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🏗 Transformer Architecture Overview

Input Tokens
     │
     ▼
Token Embedding
     │
     ▼
Positional Encoding
     │
     ▼
Transformer Encoder Layers
     │
     ├── Multi-Head Attention
     ├── Feed Forward Network
     ├── Layer Normalization
     └── Residual Connections
     │
     ▼
Transformer Decoder Layers
     │
     ├── Masked Multi-Head Attention
     ├── Encoder-Decoder Attention
     ├── Feed Forward Network
     └── Layer Normalization
     │
     ▼
Linear + Softmax
     │
     ▼
Output Tokens

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📂 Project Structure

Transformers/
│
├── Transformers.ipynb      # Full transformer implementation
├── README.md
└── images/
    └── transformer_architecture.png

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⚡ Quick Start

1️⃣ Clone the Repository

git clone https://github.com/solo938/Transformers.git

cd Transformers

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2️⃣ Install Dependencies

pip install torch numpy matplotlib

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3️⃣ Run the Notebook

jupyter notebook Transformers.ipynb

Or run it in Google Colab.

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🧩 Components Implemented

This repository implements the following transformer components:

Token Embedding

Transforms tokens into dense vector representations.

Positional Encoding

Adds position information to embeddings since transformers lack recurrence.

Multi-Head Attention

Allows the model to attend to different positions simultaneously.

Feed Forward Network

Two fully connected layers applied to each token.

Encoder Layer

Stack of attention + feedforward blocks.

Decoder Layer

Adds masked attention and encoder-decoder attention.

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🔑 Key Concepts

Self Attention

Self-attention allows the model to determine which tokens are important relative to each other.

Core attention formula:

Attention(Q, K, V) = softmax(QKᵀ / √dₖ)V

Where:

• Q = Queries
• K = Keys
• V = Values
• dₖ = key dimension

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📊 Transformer Pipeline

Text Input
     │
     ▼
Tokenization
     │
     ▼
Embedding Layer
     │
     ▼
Positional Encoding
     │
     ▼
Encoder Stack
     │
     ▼
Decoder Stack
     │
     ▼
Linear Layer
     │
     ▼
Softmax
     │
     ▼
Next Token Prediction

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📈 Possible Improvements

You can extend this project by adding:

• Training loop
• Dataset loader
• Language modeling task
• Machine translation example
• Attention visualization
• Modular PyTorch implementation
• Integration with HuggingFace datasets

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🧪 Example Use Cases

This transformer implementation can be adapted for:

• Language translation
• Text generation
• Question answering
• Chatbots
• Document summarization

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📚 Learning Resources

Recommended papers and libraries to explore:

Papers

• Attention Is All You Need
• BERT: Pre-training of Deep Bidirectional Transformers
• GPT: Generative Pre-trained Transformer

Libraries

• PyTorch
• Hugging Face Transformers
• TensorFlow

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🛣 Roadmap

• Implement training loop
• Train on a small dataset
• Add attention visualization
• Convert notebook into modular code
• Build a mini GPT model

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Submit a pull request

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⭐ Support

If you found this project helpful:

⭐ Star the repository
🍴 Fork it
🧠 Experiment with your own transformer architectures

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📜 License

MIT License