Also known as: parameter
Параметры — это внутренние значения модели, такие как веса и смещения, которые изучаются или корректируются во время обучения. Они определяют, как модель преобразует входные данные в выходные, и отличаются от гиперпараметров, которые настраивают сам процесс обучения.
Internal variables that a model uses to determine how the model processes input data and how it generates outputs. During training, you can adjust model parameters, such as weight and bias, to optimize the model's performance. During inference, you can influence the model's output through various prompting parameters, which doesn't directly change the learned model parameters. Key prompting parameters include temperature, topP, topK, and maxOutputTokens.
The weights and biases that a model learns during training. For example, in a linear regression model, the parameters consist of the bias (b ) and all the weights (w~1~, w~2~, and so on) in the following formula: In contrast, hyperparameters are the values that you (or a hyperparameter tuning service) supply to the model. For example, learning rate is a hyperparameter.