What is the difference between model hyperparameters and model parameters?
Hyperparameters and parameters are often used interchangeably but there is a difference between them. You call something a 'hyperparameter' if it cannot be learned within the estimator directly. However, 'parameters' is  more general term. When you say 'passing the parameters to the model', it generally means a combination of hyperparameters along with some other parameters that are not directly related to your estimator but are required for your model.

For example, suppose your are building an SVM classifier in sklearn:

from sklearn import svm
X = [[0, 0], [1, 1]]
y = [0, 1]
clf = svm.SVC(C =0.01, kernel ='rbf', random_state=33)
clf.fit(X, y) 


In the above code an instance of SVM is your estimator for your model for which the hyperparameters, in this case, are C and kernel. But your model has another parameter which is not a hyperparameter and that is random_state.