I Built a BPE Tokenizer From Scratch in Python And Finally Understand How LLMs Work

2026-05-28 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Novice, quick

Summary

An individual with basic Python skills, not a machine learning engineer, successfully constructed a Byte Pair Encoding (BPE) tokenizer from scratch in Python. This project aimed to demystify the internal workings of Large Language Models (LLMs), specifically the process between user input and model output. The BPE algorithm, which is fundamental to models such as ChatGPT, GPT-4, and Qwen3, is presented as a simpler and more elegant concept than initially perceived. The article serves as a guide for those curious about how LLMs process text, beginning with a definition of "token" and "tokenization" as "separating out words and word parts from running text," drawing from "Speech and Language Processing."

Key takeaway

For software engineers or AI students seeking to grasp LLM fundamentals, building a Byte Pair Encoding (BPE) tokenizer from scratch offers a direct path to understanding text processing. Your effort in implementing this core algorithm, used in models like ChatGPT and GPT-4, will demystify how LLMs interpret input and generate responses, providing practical insight beyond theoretical knowledge. This hands-on approach clarifies the often-abstract concept of tokens and their role.

Key insights

Building a BPE tokenizer from scratch demystifies how LLMs process text, revealing its underlying simplicity.

Principles

• Tokenization breaks text into subword units.
• BPE is a core LLM tokenization algorithm.
• Building BPE clarifies LLM text processing.

In practice

• Implement BPE for LLM comprehension.
• Apply BPE for efficient text encoding.
• Study subword tokenization algorithms.

Topics

• BPE Tokenization
• Large Language Models
• Python Programming
• Natural Language Processing
• Subword Tokenization
• ChatGPT
• GPT-4

Best for: AI Student, Software Engineer

Related on AIssential

• Your LLM Has Never Read a Single Word — How Tokenization Grinds Text Into Numbers — LLMs process numeric tokens, not words or letters, leading to inherent limitations like character blindness and variable processing costs across languages.
• Build an LLM from Scratch 2: Working with text data — Text data preparation for LLMs involves tokenization, converting tokens to IDs, and generating contextual embeddings.
• How Machines Understand Words: Token Classification in NLP — Token classification is foundational for NLP, enabling machines to interpret language by labeling individual words and phrases.
• Building a Small Language Model (1) — Understanding Transformer — LLMs predict the next token by processing numerical representations of text through a multi-stage Transformer architecture.
• Token Embeddings : From numbers to meaning — Token embeddings convert arbitrary token IDs into dense, learned vectors that capture semantic relationships through contextual co-occurrence.
• Cross-Tokenizer LLM Distillation through a Byte-Level Interface — Byte-Level Distillation (BLD) simplifies cross-tokenizer knowledge transfer by using a universal byte-level interface.

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