Models & Architectures
Glossary
Parameters
The internal numbers a model learns during training; their count is a rough measure of model size.
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Parameters are the learned weights and biases inside a neural network. Training adjusts them to minimise error on the data. When people say a model has 7 billion or 70 billion parameters, they mean the number of these values. More parameters usually mean more capacity, but also more memory and compute, so smaller well-trained models often beat larger ones on specific tasks.
Related terms
Neural Network
A computing model made of connected nodes organised in layers that learn by adjusting the strength of their connections.
Training Data
The examples a model learns from, whose quality and coverage shape its behaviour.
Foundation Model
A large model pre-trained on broad data that can be adapted to many downstream tasks.
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