diff --git a/docs/tutorials/multimodal/advanced_topics/continuous_training.ipynb b/docs/tutorials/multimodal/advanced_topics/continuous_training.ipynb
index b6dc654c496..539d8f326bd 100644
--- a/docs/tutorials/multimodal/advanced_topics/continuous_training.ipynb
+++ b/docs/tutorials/multimodal/advanced_topics/continuous_training.ipynb
@@ -22,7 +22,7 @@
     "\n",
     "Sometimes, the model could benefit from more training epochs or additional training time in case of underfitting. With AutoMM, you can easily extend the training time of your model without starting from scratch.\n",
     "\n",
-    "Additionally, it's also common to need to incorporate new data into your model. Whether it's more data of the same type, a dataset with a different problem type, or even a dataset with a different set of labels, AutoMM allows you to continue training seamlessly. This flexibility makes it easy to improve and adapt your models as your data grows or evolves."
+    "Additionally, it's also common to need to incorporate more data into your model. AutoMM allows you to continue training with data of the same problem type and same classes if it is a multiclass problem. This flexibility makes it easy to improve and adapt your models as your data grows."
    ]
   },
   {
@@ -110,9 +110,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import copy\n",
-    "\n",
-    "predictor_2 = copy.deepcopy(predictor)\n",
+    "predictor_2 = MultiModalPredictor.load(model_path)  # you can also use the `predictor` we assigned above\n",
     "train_data_2 = train_data.drop(train_data_1.index).sample(n=subsample_size, random_state=0)\n",
     "predictor_2.fit(train_data_2, time_limit=60)"
    ]
@@ -148,7 +146,7 @@
    "source": [
     "### Use Case 3: Applying Pre-Trained Models to New Tasks\n",
     "\n",
-    "Often, you'll encounter situations where a new task is related but not identical to a task you've previously trained a model for (e.g., training a more fine-grained sentiment analysis model). If you wish to leverage the knowledge that the model has already learned from the old data to help it learn the new task more quickly and effectively, AutoMM supports dumping your trained models into model weights and using them as foundation models:"
+    "Often, you'll encounter situations where a new task is related but not identical to a task you've previously trained a model for (e.g., training a more fine-grained sentiment analysis model, or adding more classes to your multiclass model). If you wish to leverage the knowledge that the model has already learned from the old data to help it learn the new task more quickly and effectively, AutoMM supports dumping your trained models into model weights and using them as foundation models:"
    ]
   },
   {