Build an LLM from Scratch

[th3tuga]

Build an LLM from Scratch

An interactive, end-to-end guide to understanding how Large Language Models work — from raw text to generated output.

🤖 What is a Large Language Model?

A Large Language Model (LLM) like ChatGPT is, at its core, a next-token prediction machine. Given a sequence of text, it predicts the most probable next word (or subword). It does this by learning statistical patterns from massive amounts of text data.

Under the hood, an LLM is built from a transformer architecture — a specific type of neural network invented in 2017. The transformer's key innovation is self-attention: a mechanism that lets every word in a sentence "look at" every other word to understand context.

In this interactive guide, you'll build every component of a working LLM from scratch. You'll see the real math, run real code, and train a real model — not just read about it. The model is small (~550K parameters vs. GPT-3's 175 billion), but the architecture is identical. Every concept here scales directly to the largest models.

🧭 How This App Works

This is not a passive tutorial — it's a live laboratory. A real PyTorch model runs on the backend. When you type text on any page, it flows through the actual model pipeline. When you train, real gradients update real weights. When you generate, the model produces real predictions.

https://jayvisaria.github.io/LLM-Visualizer/#/dashboard