Parameter
One of the internal numbers a model adjusts during training; parameter count is a rough proxy for model capacity.
Parameters are the learnable values inside a model — mostly weights and biases. Training is the process of nudging them until the model’s outputs stop being wrong. Headline counts like ‘70B parameters’ describe capacity, not quality: architecture, data, and post-training often matter more than raw size.
In practice: A 70B model has roughly 70 billion adjustable numbers; a bigger number does not automatically mean better answers.