Building LLM From Scratch: Understanding How Large Language Models Work

2026-05-22 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

This article details a project to build a small GPT-style language model from scratch using PyTorch, aiming to demystify the core mechanics of Large Language Models. It explains how text is converted into numerical tokens, how embedding layers assign meaning, and how positional embeddings convey word order. The analysis delves into self-attention for contextual understanding and multi-head attention for diverse relationship learning, followed by feed-forward networks for deeper pattern recognition, culminating in an output layer for next-token prediction. The training process involves using a dataset like Tiny Shakespeare, tokenizing text, creating input/target sequences, and iteratively refining predictions through loss calculation and backpropagation. While not a production-level chatbot, the Mini LLM demonstrates fundamental language pattern learning.

Key takeaway

For AI Engineers or students seeking to demystify LLM mechanics, this project offers a practical blueprint. By building a small GPT-style model from scratch, you gain direct insight into tokenization, embeddings, attention mechanisms, and the iterative training process. This hands-on approach clarifies how LLMs learn to predict and generate text, providing a foundational understanding essential for working with larger, more complex models. You should consider replicating this Mini LLM to solidify your grasp of core transformer architecture principles.

Key insights

Large Language Models fundamentally predict the next token by processing numerical representations of text through attention mechanisms.

Principles

• LLMs fundamentally predict the next token.
• Text must convert to numbers for neural networks.
• Self-attention provides crucial token context.

Method

Building a Mini LLM involves tokenization, embedding, positional encoding, transformer blocks (self-attention, multi-head, feed-forward), and an output layer for next-token prediction, trained via backpropagation on text datasets.

In practice

• Implement character-level tokenization.
• Utilize PyTorch for model construction.

Topics

• Large Language Models
• Transformer Architecture
• Self-Attention
• PyTorch
• Text Generation
• Tokenization

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

Related on AIssential

• LLMs Made Simple: Examples, Analogies & Memory Tricks — LLMs are advanced autocomplete systems predicting tokens to generate human-like language from vast training data.
• Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs — Training an LLM from scratch involves understanding core components like tokenization, transformer architecture, and the training loop.
• Building an LLM From Scratch: The Mechanism That Changed AI Forever, Implemented From Zero — Self-attention enables LLMs to understand word relevance by dynamically weighting contextual dependencies.
• Moving To Substack — The Transformer architecture is fundamental to modern generative AI, enabling advanced language model capabilities.
• I Built a Language Model From Scratch on My MacBook — No GPU, No Cloud, No Excuses — Building a small LLM from scratch reveals the core mechanics of all GPT-style models, demystifying their operation.
• Building LLMs from the Ground Up: A 3-hour Coding Workshop — Building LLMs from scratch, including data preparation, architecture, pre-training, and fine-tuning, enhances fundamental understanding.

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.