Also known as: parameter
Los parámetros son valores internos del modelo, como pesos y sesgos, que se aprenden o ajustan durante el entrenamiento. Determinan cómo un modelo transforma entradas en salidas y se distinguen de los hiperparámetros, que configuran el proceso de aprendizaje.
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.