diff --git a/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_activation_threshold_search.ipynb b/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_activation_threshold_search.ipynb
index 9d0a50a17..ddaa4a05a 100644
--- a/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_activation_threshold_search.ipynb
+++ b/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_activation_threshold_search.ipynb
@@ -340,7 +340,7 @@
     "\n",
     "MCT’s `quantization_info` stores the threshold values for each layer. However, to view the actual activation distributions, the model needs to be reconstructed up to and including the target layer selected for visualization.\n",
     "\n",
-    "To do this, we first need to identify the layer names. In Keras, this can be easily done for the first 10 layers using the following code snippet."
+    "To do this, we first need to identify the layer names. "
    ]
   },
   {
@@ -353,8 +353,8 @@
    "outputs": [],
    "source": [
     "for index, (name, layer) in enumerate(float_model.named_modules()):\n",
-    "    if index < 10:\n",
-    "        print(name, layer)\n",
+    "    if index < 14:\n",
+    "        print(name)\n",
     "    else:\n",
     "        break"
    ]