Weights, training and inference

The scores from chapter 1 come from billions of numbers called weights, fixed during a training run that finished before the model was released. Training and inference are separate events: training is where the behaviour came from, inference is the request you send, and your request reads the weights without writing to them. This chapter sizes the fixed artefact, contrasts the two events field by field, runs a four-minute experiment that settles the question, and works through what the separation means for corrections, knowledge cutoffs and 'giving the model our data'.

What a language model is — read the current version at https://daviddominguez.dev/learn/en/what-a-language-model-is/weights-training-inference

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