⚡ SwiftTransformer V1

SwiftTransformer V1 is a lightweight and efficient Transformer-based model designed for rapid experimentation, learning, and prototyping. Built with simplicity and scalability in mind, it provides a clean implementation of core attention mechanisms without unnecessary complexity.

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🚀 Features

• ⚡ Lightweight and fast Transformer architecture
• 🧠 Core components: Embedding, Positional Encoding, Multi-Head Attention
• 🔧 Easy to understand and modify
• 📈 Scalable for future improvements (GPT-style, large datasets)
• 🧪 Ideal for learning and experimentation

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🧠 Architecture Overview

SwiftTransformer V1 includes:

• Token Embedding
• Learnable Positional Encoding
• Transformer Encoder (Multi-Head Attention + Feed Forward)
• Linear Output Layer

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📁 Project Structure

SwiftTransformer/
│
├── data/                  # Training data
│   └── sample.txt
│
├── model/                 # Core model
│   └── swift_transformer.py
│
├── train/                 # Training scripts
│   └── train.py
│
├── utils/                 # Utilities
│   └── tokenizer.py
│
├── config.py              # Configuration
├── requirements.txt       # Dependencies
└── README.md              # Project documentation

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⚙️ Installation

git clone https://github.com/your-username/SwiftTransformer.git
cd SwiftTransformer
pip install -r requirements.txt

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🧪 Usage

1. Prepare Dataset

Edit:

data/sample.txt

Example:

hello swift transformer this is version one

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2. Train Model

python train/train.py

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3. Output

Epoch 0, Loss: 6.12
Epoch 1, Loss: 5.87
...

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🔥 Example Code

from model.swift_transformer import SwiftTransformer
import torch

model = SwiftTransformer(vocab_size=1000)
x = torch.randint(0, 1000, (2, 20))

output = model(x)
print(output.shape)

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📈 Roadmap

✅ V1 (Current)

• Basic Transformer architecture
• Simple tokenizer
• Training pipeline

🔜 V2

• Causal Mask (GPT-style)
• Text generation
• Save & load model

🚀 V3

• Large-scale dataset
• Fine-tuning
• Web demo (interactive UI)

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💡 Use Cases

• NLP experiments
• Sequence prediction
• Educational purposes
• Rapid prototyping of AI models

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🧑‍💻 Author

Thunders Foundation Aspiring AI Engineer focused on building future-ready intelligent systems.

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⭐ Contributing

Contributions are welcome! Feel free to fork this repository and submit pull requests.

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📜 License

MIT License

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⚡ Final Note

SwiftTransformer V1 is not just a model — it's a starting point. Build fast, iterate faster, and evolve it into something powerful.