Under Development
Please note: This section is a work in progress. Information and features are subject to change and may be incomplete.
GPT-Neo: Transformer Implementation
Complete implementation of the 'Attention is All You Need' paper, building a GPT-style language model from scratch with detailed documentation and a training pipeline.
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.