diff --git a/ch07/04_preference-tuning-with-dpo/create-preference-data-ollama.ipynb b/ch07/04_preference-tuning-with-dpo/create-preference-data-ollama.ipynb
index 2142703..a8721b6 100644
--- a/ch07/04_preference-tuning-with-dpo/create-preference-data-ollama.ipynb
+++ b/ch07/04_preference-tuning-with-dpo/create-preference-data-ollama.ipynb
@@ -40,9 +40,7 @@
     "  1. We use the instruction-finetuned LLM to generate multiple responses and have humans rank them based on their preference and/or given preference criteria\n",
     "  2. We use the instruction-finetuned LLM to generate multiple responses and have LLMs rank them based on given preference criteria\n",
     "  3. We use an LLM to generate preferred and dispreferred responses given certain preference criteria\n",
-    "- In this notebook, we take approach 3\n",
-    "\n",
-    "\n",
+    "- In this notebook, we consider approach 3\n",
     "- This notebook uses a 70 billion parameter Llama 3.1-Instruct model through ollama to generate preference labels for an instruction dataset\n",
     "- The expected format of the instruction dataset is as follows:\n",
     "\n",
@@ -589,7 +587,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,