diff --git a/intermediate_source/model_parallel_tutorial.py b/intermediate_source/model_parallel_tutorial.py
index 515b689301a..df43f6fa5f9 100644
--- a/intermediate_source/model_parallel_tutorial.py
+++ b/intermediate_source/model_parallel_tutorial.py
@@ -86,7 +86,7 @@ def forward(self, x):
 #
 # It is also possible to run an existing single-GPU module on multiple GPUs
 # with just a few lines of changes. The code below shows how to decompose
-# ``torchvision.models.reset50()`` to two GPUs. The idea is to inherit from
+# ``torchvision.models.resnet50()`` to two GPUs. The idea is to inherit from
 # the existing ``ResNet`` module, and split the layers to two GPUs during
 # construction. Then, override the ``forward`` method to stitch two
 # sub-networks by moving the intermediate outputs accordingly.
@@ -136,7 +136,7 @@ def forward(self, x):
 #
 # Let us run an experiment to get a more quantitative view of the execution
 # time. In this experiment, we train ``ModelParallelResNet50`` and the existing
-# ``torchvision.models.reset50()`` by running random inputs and labels through
+# ``torchvision.models.resnet50()`` by running random inputs and labels through
 # them. After the training, the models will not produce any useful predictions,
 # but we can get a reasonable understanding of the execution times.