A Keras model is a way to organize layers in a Deep Learning model. It is not possible to directly remove layers from a model, but it is possible to create a new model that is a submodel of the original model, using the layers of the original model that you want to keep.

Here is an example of how to do this in Python using the Keras library:

from keras.models import Model

# Create a model with the first n layers of the original model
submodel = Model(inputs=original_model.input, outputs=original_model.layers[n].output)

# Compile the submodel
submodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Use the submodel to make predictions
predictions = submodel.predict(x_test)

This will create a new model that consists of the first n layers of the original model. The submodel can then be used to make predictions or to fine-tune the model further.

Note that this approach only works if the layers you want to keep are connected in a sequential manner. If the layers you want to keep are not connected sequentially, you will need to use a different approach to create the submodel.