You could change the p attribute, if you’ve created an nn.Dropout module:
# Train with your initial dropout
...

# Change to new value and continue training
model.drop.p = 0.1

or you could alternatively use the functional API and pass p into forward:
def forward(self, x, p):
    ...
    x = F.dropout(x, p=p, training=self.training)

Don’t forget to use the self.training attribute from the parent model in the functional call. Otherwise you won’t disable the dropout call after calling model.eval().