How a Tiny LLM Works: Building a Language Model From Scratch in PyTorch

Have you ever wondered what actually happens inside an LLM when you type:

"Hello, how are"

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and it predicts:

"you"

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We often hear words like Transformer, Attention, Tokens, RoPE, GQA, RMSNorm, and SwiGLU .

But what do these things actually do?

I recently worked through the implementation of a small language model from scratch, and I found that the easiest way to understand an LLM is to follow one piece of text through the entire model .

So in this post, we'll do exactly that.

By the end, you should understand the basic flow:

Text ↓ Tokens ↓ Embeddings ↓ Transformer ↓ Attention ↓ Feed Forward Network ↓ Prediction ↓ Next Token

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Let's break it down step by step.

1.…