Transformer from scratch

1. 1Build a character-level GPT transformer model from scratch using PyTorch
2. 2Implement the transformer architecture, including the encoder and decoder components
3. 3Train the model on a dataset of text using a suitable loss function and optimizer
4. 4Test the model's performance on a validation set and evaluate its character-level prediction accuracy
5. 5Compare the results with pre-trained transformer models and analyze the differences

This tutorial is beneficial for machine learning engineers and researchers who want to understand the inner workings of transformer models and implement them from scratch. It can also be useful for software engineers interested in natural language processing and PyTorch

💡 Implementing a transformer model from scratch can provide valuable insights into its architecture and training, and can be used as a foundation for more complex NLP tasks

🤖 Build a character-level GPT transformer from scratch in PyTorch! 💻