diff --git a/advanced_source/dynamic_quantization_tutorial.py b/advanced_source/dynamic_quantization_tutorial.py
index c8d94789d5d..c5b7d70a046 100644
--- a/advanced_source/dynamic_quantization_tutorial.py
+++ b/advanced_source/dynamic_quantization_tutorial.py
@@ -134,10 +134,18 @@ def tokenize(self, path):
 # -----------------------------
 #
 # This is a tutorial on dynamic quantization, a quantization technique
-# that is applied after a model has been trained. Therefore, we'll simply load some
-# pretrained weights into this model architecture; these weights were obtained
-# by training for five epochs using the default settings in the word language model
-# example.
+# that is applied after a model has been trained. Therefore, we'll simply
+# load some pretrained weights into this model architecture; these
+# weights were obtained by training for five epochs using the default
+# settings in the word language model example.
+#
+# Before running this tutorial, download the required pre-trained model:
+#
+# .. code-block:: bash
+#
+#     wget https://s3.amazonaws.com/pytorch-tutorial-assets/word_language_model_quantize.pth
+#
+# Place the downloaded file in the data directory or update the model_data_filepath accordingly.
 
 ntokens = len(corpus.dictionary)