Build an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate Text

2025-03-17 · Source: Sebastian Raschka · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

This content details the step-by-step implementation of a GPT model architecture from scratch, focusing on its core building blocks. It begins by outlining the overall structure, including embedding layers and the masked multi-head attention module, then progressively adds components like Layer Normalization, GELU activation functions within a Feed Forward Network, and shortcut connections. Each section explains the theoretical basis and practical coding of these elements, emphasizing their role in stabilizing training and enabling complex feature extraction. The final sections integrate these components into a complete GPT model, discuss parameter counting, and demonstrate a basic text generation function, highlighting that meaningful output requires model training, which is covered in a subsequent chapter.

Key takeaway

For Machine Learning Engineers building custom LLMs, understanding the modular construction of GPT models is crucial. You should meticulously implement each component—attention, normalization, feed-forward networks, and residual connections—to ensure compatibility with pre-trained weights and optimize training dynamics. Pay close attention to parameter dimensions and weight sharing strategies to manage model size and performance effectively.

Key insights

Building a GPT model involves sequentially integrating specialized deep learning components for robust text generation.

Principles

• Nonlinear activations enable complex learning.
• Layer normalization stabilizes network training.
• Shortcut connections prevent gradient issues.

Method

The GPT architecture is constructed by combining embedding layers, masked multi-head attention, layer normalization, GELU-activated feed-forward networks, and shortcut connections, then iteratively generating tokens.

In practice

• Use `torch.no_grad()` for inference to save memory.
• Truncate inputs to `context_size` to prevent model crashes.

Topics

• GPT Model Architecture
• Transformer Block
• Layer Normalization
• GELU Activation Function
• Shortcut Connections

Best for: Machine Learning Engineer, AI Student, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Sebastian Raschka.