---
title: "GPT-Neo: Transformer Implementation"
category: "Natural Language Processing"
status: "completed"
url: "https://himanshuat.com/research/gpt-neo"
---

# GPT-Neo: Transformer Implementation

Authors: Himanshu

Published: N/A

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.

Tags: Transformers, NLP, Deep Learning, PyTorch, Implementation

Paper: https://github.com/Himasnhu-AT/gpt-neo

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Source: https://himanshuat.com/research/gpt-neo