GPT-Neo: Transformer Implementation

abstract

• A comprehensive, from-scratch implementation of the transformer architecture as described in the seminal paper 'Attention is All You Need'. This project focuses on building a decoder-only, GPT-style model.

implementation

• Built using PyTorch, the model includes multi-head self-attention, positional encoding, feed-forward networks, and layer normalization. The repository also contains a complete training and inference pipeline.

results

• The model was successfully trained on various text corpora, demonstrating its ability to generate coherent text and understand context. It serves as a strong educational baseline for transformer architectures.

learnings

• Gained a deep, practical understanding of attention mechanisms, model architecture, and the challenges of training large language models, including managing computational resources and preventing overfitting.

How it works

Decoder-only transformer block, implemented from scratch.