From noise to knowing: a language model taking shape
Watch a transformer assemble itself — tokens become vectors, layers stack, and gradient descent crystallizes random weights into a model that can answer.
An illustrative visualization — geometry, not literal weights. Architecture figures are approximate.
Void· 0 / 4
Random initialization
Before training, a model is pure noise — millions of random numbers with no structure and no ability to predict anything.