Fixes (#14534)

docs/source/quicktour.rst

@@ -346,35 +346,43 @@ Once your model is fine-tuned, you can save it with its tokenizer in the followi
 
 .. code-block::
 
+    >>> ## PYTORCH CODE
     >>> pt_save_directory = './pt_save_pretrained'
     >>> tokenizer.save_pretrained(pt_save_directory)
     >>> pt_model.save_pretrained(pt_save_directory)
-
-.. code-block::
-
+    >>> ## TENSORFLOW CODE
     >>> tf_save_directory = './tf_save_pretrained'
     >>> tokenizer.save_pretrained(tf_save_directory)
     >>> tf_model.save_pretrained(tf_save_directory)
 
 You can then load this model back using the :func:`~transformers.AutoModel.from_pretrained` method by passing the
 directory name instead of the model name. One cool feature of 🤗 Transformers is that you can easily switch between
-PyTorch and TensorFlow: any model saved as before can be loaded back either in PyTorch or TensorFlow. If you are
-loading a saved PyTorch model in a TensorFlow model, use :func:`~transformers.TFAutoModel.from_pretrained` like this:
+PyTorch and TensorFlow: any model saved as before can be loaded back either in PyTorch or TensorFlow.
 
-.. code-block::
 
-    >>> from transformers import TFAutoModel
-    >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
-    >>> tf_model = TFAutoModel.from_pretrained(pt_save_directory, from_pt=True)
+If you would like to load your saved model in the other framework, first make sure it is installed:
+
+.. code-block:: bash
 
-and if you are loading a saved TensorFlow model in a PyTorch model, you should use the following code:
+    ## PYTORCH CODE
+    pip install tensorflow
+    ## TENSORFLOW CODE
+    pip install torch
+
+Then, use the corresponding Auto class to load it like this:
 
 .. code-block::
 
+    ## PYTORCH CODE
+    >>> from transformers import TFAutoModel
+    >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
+    >>> tf_model = TFAutoModel.from_pretrained(pt_save_directory, from_pt=True)
+    ## TENSORFLOW CODE
     >>> from transformers import AutoModel
     >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
     >>> pt_model = AutoModel.from_pretrained(tf_save_directory, from_tf=True)
 
+
 Lastly, you can also ask the model to return all hidden states and all attention weights if you need them: