diff --git a/ch07/04_preference-tuning-with-dpo/dpo-from-scratch.ipynb b/ch07/04_preference-tuning-with-dpo/dpo-from-scratch.ipynb
index c8773b1..29c5d6e 100644
--- a/ch07/04_preference-tuning-with-dpo/dpo-from-scratch.ipynb
+++ b/ch07/04_preference-tuning-with-dpo/dpo-from-scratch.ipynb
@@ -131,7 +131,7 @@
     "- In other words, DPO focuses on directly optimizing the model's output to align with human preferences or specific objectives\n",
     "- Shown below is the main idea as an overview of how DPO works\n",
     "\n",
-    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/dpo/5.webp\" width=600px>"
+    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/dpo/5.webp?123\" width=600px>"
    ]
   },
   {
@@ -143,7 +143,7 @@
    "source": [
     "- The concrete equation to implement the DPO loss is shown below; we will revisit the equation when we implement it in Python further down in this code notebook\n",
     "\n",
-    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/dpo/3.webp\" width=600px>"
+    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/dpo/3.webp?123\" width=600px>"
    ]
   },
   {
@@ -1807,7 +1807,7 @@
     "- Note that the DPO loss code below is based on the method proposed in the [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://arxiv.org/abs/2305.18290) paper\n",
     "- For reference, the core DPO equation is shown again below:\n",
     "\n",
-    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/dpo/3.webp\" width=800px>\n",
+    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/dpo/3.webp?123\" width=800px>\n",
     "\n",
     "- In the equation above,\n",
     " - \"expected value\" $\\mathbb{E}$ is statistics jargon and stands for the average or mean value of the random variable (the expression inside the brackets)\n",
@@ -3088,7 +3088,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,