AI Vocabulary · 01 The Model
What is Parameters?
The billions of numbers inside a model, set during training. Everything the model knows is stored in them.
Parameters, also called weights, are the numbers inside a model. Training adjusts them until the model predicts text well. There is no database of facts inside: whatever the model "knows" is spread across these numbers.
Parameter counts are often quoted in billions (for example "8B" or "70B"). More parameters usually mean more capability but also more memory, more computing power and higher cost. Size alone is not a reliable measure of quality.
Once training is finished, the parameters are frozen. Nothing you type changes them. Only further training, such as fine-tuning, does.
Why it matters for you
Parameter count tells you roughly what hardware a model needs and what it may cost to run. It does not tell you whether the model is good at your task. Test on your own examples.
Own it
When a company publishes its parameters, the model becomes an open-weight model that you can run yourself. Holding the weights is the most concrete form of owning a model.
Where it comes from
"Parameter" joins the Greek para, "beside", and metron, "measure". It appeared in the 1650s in geometry, as a constant that defines a curve, and only later came to mean any measurable factor of a system. Modern AI stretched it to astonishing numbers: GPT-3 in 2020 had 175 billion parameters. Etymonline: parameter
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