I trained my own transformer model from the ground up — no HuggingFace, no shortcuts. Here's the full breakdown: multi-head attention, positional encoding, training loops, and the mistakes that actually taught me how these things work.
Why Build from Scratch?
There's a massive gap between using a transformer and understanding one. I spent months fine-tuning pre-trained models through APIs — Groq, Gemini, Mistral — for Manshverse. But I kept hitting this wall: when something broke, I couldn't reason about why. I was treating the model as a black box.
So I decided to build one from scratch. Not a toy implementation from a tutorial. An actual trainable transformer architecture, written in pure PyTorch, that I could train on my own data and study the loss curves of. The goal wasn't to compete with GPT — it was to understand the machine.
If you can't build it from scratch, you don't really understand it. — Richard Feynman (paraphrased)
