diff --git a/tutorials/notebooks/pytorch/ptq/pytorch_yolov8n_for_imx500.ipynb b/tutorials/notebooks/pytorch/ptq/pytorch_yolov8n_for_imx500.ipynb
index c7f9c178b..7526b7bdb 100644
--- a/tutorials/notebooks/pytorch/ptq/pytorch_yolov8n_for_imx500.ipynb
+++ b/tutorials/notebooks/pytorch/ptq/pytorch_yolov8n_for_imx500.ipynb
@@ -2,6 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "fab9d9939dc74da4",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "# YOLOv8n Object Detection PyTorch Model - Quantization for IMX500\n",
     "\n",
@@ -21,76 +25,78 @@
     "1. Post-Training Quantization using MCT of PyTorch object detection model.\n",
     "2. Data preparation - loading and preprocessing validation and representative datasets from COCO.\n",
     "3. Accuracy evaluation of the floating-point and the quantized models."
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "fab9d9939dc74da4"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Setup\n",
-    "### Install the relevant packages"
-   ],
+   "id": "d74f9c855ec54081",
    "metadata": {
     "collapsed": false
    },
-   "id": "d74f9c855ec54081"
+   "source": [
+    "## Setup\n",
+    "### Install the relevant packages"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "7c7fa04c9903736f",
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "!pip install -q torch\n",
+    "!pip install onnx\n",
     "!pip install -q pycocotools\n",
+    "!pip install huggingface-hub>=0.21\n",
     "!pip install --pre sony-custom-layers-dev==0.2.0.dev1"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "7c7fa04c9903736f"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    " Clone a copy of the [MCT](https://github.com/sony/model_optimization) (Model Compression Toolkit) into your current directory. This step ensures that you have access to [MCT Models Library](https://github.com/sony/model_optimization/tree/main/tutorials/mct_model_garden) folder which contains all the necessary utility functions for this tutorial.\n",
-    "  **It's important to note that we use the most up-to-date MCT code available.**"
-   ],
+   "id": "57717bc8f59a0d85",
    "metadata": {
     "collapsed": false
    },
-   "id": "57717bc8f59a0d85"
+   "source": [
+    " Clone a copy of the [MCT](https://github.com/sony/model_optimization) (Model Compression Toolkit) into your current directory. This step ensures that you have access to [MCT Models Library](https://github.com/sony/model_optimization/tree/main/tutorials/mct_model_garden) folder which contains all the necessary utility functions for this tutorial.\n",
+    "  **It's important to note that we use the most up-to-date MCT code available.**"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "9728247bc20d0600",
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "!git clone https://github.com/sony/model_optimization.git local_mct\n",
     "!pip install -r ./local_mct/requirements.txt\n",
     "import sys\n",
     "sys.path.insert(0,\"./local_mct\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "9728247bc20d0600"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Download COCO evaluation set"
-   ],
+   "id": "7a1038b9fd98bba2",
    "metadata": {
     "collapsed": false
    },
-   "id": "7a1038b9fd98bba2"
+   "source": [
+    "### Download COCO evaluation set"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "8bea492d71b4060f",
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "!wget -nc http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
@@ -99,11 +105,7 @@
     "!wget -nc http://images.cocodataset.org/zips/val2017.zip\n",
     "!unzip -q -o val2017.zip -d ./coco\n",
     "!echo Done loading val2017 images"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "8bea492d71b4060f"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -119,15 +121,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "e8395b28-4732-4d18-b081-5d3bdf508691",
    "metadata": {
     "is_executing": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'tutorials/mct_model_garden/models_pytorch/yolov8/yolov8n.yaml'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[2], line 2\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mtutorials\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mmct_model_garden\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mmodels_pytorch\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01myolov8\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01myolov8\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m DetectionModelPyTorch, yaml_load\n\u001B[0;32m----> 2\u001B[0m cfg_dict \u001B[38;5;241m=\u001B[39m \u001B[43myaml_load\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mtutorials/mct_model_garden/models_pytorch/yolov8/yolov8n.yaml\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mappend_filename\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# model dict\u001B[39;00m\n\u001B[1;32m      3\u001B[0m model \u001B[38;5;241m=\u001B[39m DetectionModelPyTorch\u001B[38;5;241m.\u001B[39mfrom_pretrained(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mSSI-DNN/pytorch_yolov8n_640x640_bb_decoding\u001B[39m\u001B[38;5;124m\"\u001B[39m, cfg\u001B[38;5;241m=\u001B[39mcfg_dict)\n",
+      "File \u001B[0;32m/data/projects/swat/users/ariell/repos/my_fork/yolov8_pytorch_tut/model_optimization/tutorials/mct_model_garden/models_pytorch/yolov8/yolov8.py:51\u001B[0m, in \u001B[0;36myaml_load\u001B[0;34m(file, append_filename)\u001B[0m\n\u001B[1;32m     40\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21myaml_load\u001B[39m(file: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdata.yaml\u001B[39m\u001B[38;5;124m'\u001B[39m, append_filename: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Dict[\u001B[38;5;28mstr\u001B[39m, \u001B[38;5;28many\u001B[39m]:\n\u001B[1;32m     41\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m     42\u001B[0m \u001B[38;5;124;03m    Load YAML data from a file.\u001B[39;00m\n\u001B[1;32m     43\u001B[0m \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m     49\u001B[0m \u001B[38;5;124;03m        dict: YAML data and file name.\u001B[39;00m\n\u001B[1;32m     50\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m---> 51\u001B[0m     \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mfile\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mignore\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mutf-8\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mas\u001B[39;00m f:\n\u001B[1;32m     52\u001B[0m         s \u001B[38;5;241m=\u001B[39m f\u001B[38;5;241m.\u001B[39mread()  \u001B[38;5;66;03m# string\u001B[39;00m\n\u001B[1;32m     53\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m s\u001B[38;5;241m.\u001B[39misprintable():  \u001B[38;5;66;03m# remove special characters\u001B[39;00m\n",
+      "\u001B[0;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: 'tutorials/mct_model_garden/models_pytorch/yolov8/yolov8n.yaml'"
+     ]
+    }
+   ],
    "source": [
     "from tutorials.mct_model_garden.models_pytorch.yolov8.yolov8 import DetectionModelPyTorch, yaml_load\n",
-    "cfg_dict = yaml_load(\"yolov8n.yaml\", append_filename=True)  # model dict\n",
+    "cfg_dict = yaml_load(\"./local_mct/tutorials/mct_model_garden/models_pytorch/yolov8/yolov8n.yaml\", append_filename=True)  # model dict\n",
     "model = DetectionModelPyTorch.from_pretrained(\"SSI-DNN/pytorch_yolov8n_640x640_bb_decoding\", cfg=cfg_dict)"
    ]
   },
@@ -146,6 +161,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "56393342-cecf-4f64-b9ca-2f515c765942",
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import model_compression_toolkit as mct\n",
@@ -229,37 +248,33 @@
     "                                    score_threshold=score_threshold,\n",
     "                                    iou_threshold=iou_threshold,\n",
     "                                    max_detections=max_detections).to(device=device)"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "56393342-cecf-4f64-b9ca-2f515c765942"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "3be2016acdc9da60",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "### Model Export\n",
     "\n",
     "Now, we can export the quantized model, ready for deployment, into a `.onnx` format file. Please ensure that the `save_model_path` has been set correctly. "
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "3be2016acdc9da60"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "72dd885c7b92fa93",
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "mct.exporter.pytorch_export_model(model=quant_model_pp,\n",
     "                                  save_model_path='./qmodel_pp.onnx',\n",
     "                                  repr_dataset=representative_dataset_gen)"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "72dd885c7b92fa93"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -347,6 +362,10 @@
   },
   {
    "cell_type": "markdown",
+   "id": "6d93352843a27433",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "\\\n",
     "Copyright 2024 Sony Semiconductor Israel, Inc. All rights reserved.\n",
@@ -362,14 +381,13 @@
     "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
     "See the License for the specific language governing permissions and\n",
     "limitations under the License."
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "6d93352843a27433"
+   ]
   }
  ],
  "metadata": {
+  "colab": {
+   "provenance": []
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -385,10 +403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "colab": {
-   "provenance": []
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,