diff --git a/ch07/01_main-chapter-code/ch07.ipynb b/ch07/01_main-chapter-code/ch07.ipynb
index e4cf66a..59917ed 100644
--- a/ch07/01_main-chapter-code/ch07.ipynb
+++ b/ch07/01_main-chapter-code/ch07.ipynb
@@ -28,17 +28,9 @@
     "# Chapter 7: Finetuning To Follow Instructions"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "a984b9ef-af93-415a-9ec7-97385f28af7b",
-   "metadata": {},
-   "source": [
-    "- Comments & notes in progress ..."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "4e19327b-6c02-4881-ad02-9b6d3ec0b1b4",
    "metadata": {
     "colab": {
@@ -52,11 +44,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "matplotlib version: 3.8.4\n",
-      "tiktoken version: 0.6.0\n",
+      "matplotlib version: 3.8.2\n",
+      "tiktoken version: 0.5.1\n",
       "torch version: 2.2.2\n",
-      "tqdm version: 4.66.2\n",
-      "tensorflow version: 2.16.1\n"
+      "tqdm version: 4.66.1\n",
+      "tensorflow version: 2.15.0\n"
      ]
     }
    ],
@@ -82,6 +74,36 @@
     "## 7.1 Introduction to instruction finetuning"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "53dba24a-6805-496c-9a7f-c75e2d3527ab",
+   "metadata": {},
+   "source": [
+    "- In chapter 5, we saw that pretraining an LLM involves a training procedure where it learns to generate one word at a time\n",
+    "- Hence, a pretrained LLM is good at text completion, but it is not good at following instructions\n",
+    "- In this chapter, we teach the LLM to better follow instructions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18dc0535-0904-44ed-beaf-9b678292ef35",
+   "metadata": {},
+   "source": [
+    "[insert figure]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4698b23-12e0-4bd7-a140-ccb3dd71d4e8",
+   "metadata": {},
+   "source": [
+    "- An optional step after instruction finetuning is preference tuning, which refines the response style of an LLM; readers interested in preference tuning can find example code in the bonus materials: [../04_preference-tuning-with-dpo](../04_preference-tuning-with-dpo)\n",
+    "\n",
+    "- The topics covered in this chapter are summarized in the figure below\n",
+    "\n",
+    "[insert figure]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "5384f0cf-ef3c-4436-a5fa-59bd25649f86",
@@ -90,9 +112,17 @@
     "## 7.2 Preparing a dataset for supervised instruction finetuning"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f8b34ff8-619f-4e89-bd03-ce513269760d",
+   "metadata": {},
+   "source": [
+    "- We will work with an instruction dataset I prepared for this chapter"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "0G3axLw6kY1N",
    "metadata": {
     "colab": {
@@ -106,7 +136,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1100\n"
+      "Number of entries: 1100\n"
      ]
     }
    ],
@@ -137,12 +167,20 @@
     "url = \"https://raw.githubusercontent.com/rasbt/LLMs-from-scratch/main/ch07/01_main-chapter-code/instruction-data.json\"\n",
     "\n",
     "data = download_and_load_file(file_path, url)\n",
-    "print(len(data))"
+    "print(\"Number of entries:\", len(data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7af8176-4255-4e92-8c7d-998771733eb8",
+   "metadata": {},
+   "source": [
+    "- Each item in the `data` list we loaded from the JSON file above is a dictionary in the following form:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "-LiuBMsHkzQV",
    "metadata": {
     "colab": {
@@ -156,17 +194,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'instruction': 'Evaluate the following phrase by transforming it into the spelling given.', 'input': 'freind --> friend', 'output': 'The spelling of the given phrase \"freind\" is incorrect, the correct spelling is \"friend\".'}\n"
+      "Example entry:\n",
+      "\n",
+      " {'instruction': 'Identify the correct spelling of the following word.', 'input': 'Ocassion', 'output': \"The correct spelling is 'Occasion.'\"}\n"
      ]
     }
    ],
    "source": [
-    "print(data[0])"
+    "print(\"Example entry:\\n\\n\", data[50])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5a32b34-485a-4816-a77a-da14f9fe6e46",
+   "metadata": {},
+   "source": [
+    "- Note that the `'input'` field can be empty:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "uFInFxDDk2Je",
    "metadata": {
     "colab": {
@@ -180,17 +228,137 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'instruction': \"Change the sentence 'You should have called me.' into a question.\", 'input': '', 'output': 'Should you have called me?'}\n"
+      "Another example entry:\n",
+      "\n",
+      " {'instruction': \"What is an antonym of 'complicated'?\", 'input': '', 'output': \"An antonym of 'complicated' is 'simple'.\"}\n"
      ]
     }
    ],
    "source": [
-    "print(data[-1])"
+    "print(\"Another example entry:\\n\\n\", data[999])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f034799a-6575-45fd-98c9-9d1012d0fd58",
+   "metadata": {},
+   "source": [
+    "- Instruction finetuning is often referred to as \"supervised instruction finetuning\" because it involves training a model on a dataset where the input-output pairs are explicitly provided\n",
+    "- There are different ways to format the entries as inputs to the LLM; the figure below illustrates two example formats that were used for training the Alpaca (https://crfm.stanford.edu/2023/03/13/alpaca.html) and Phi-3 (https://arxiv.org/abs/2404.14219) LLMs, respectively"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dffa4f70-44d4-4be4-89a9-2159f4885b10",
+   "metadata": {},
+   "source": [
+    "[insert figure]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd79a74e-befb-491c-be49-f777a6a5b6a6",
+   "metadata": {},
+   "source": [
+    "- In this chapter, we use Alpaca-style prompt formatting, which was the original prompt template for instruction finetuning\n",
+    "- Below we format the input that we will pass as input to the LLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "Jhk37nnJnkBh",
+   "metadata": {
+    "id": "Jhk37nnJnkBh"
+   },
+   "outputs": [],
+   "source": [
+    "def format_input(entry):\n",
+    "    instruction_text = (\n",
+    "        f\"Below is an instruction that describes a task. \"\n",
+    "        f\"Write a response that appropriately completes the request.\"\n",
+    "        f\"\\n\\n### Instruction:\\n{entry['instruction']}\"\n",
+    "    )\n",
+    "\n",
+    "    input_text = f\"\\n\\n### Input:\\n{entry['input']}\" if entry[\"input\"] else \"\"\n",
+    "\n",
+    "    return instruction_text + input_text"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "F9UQRfjzo4Js",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "F9UQRfjzo4Js",
+    "outputId": "b56e6c03-f603-4e9d-c1b6-b4a70403caf9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
+      "\n",
+      "### Instruction:\n",
+      "Identify the correct spelling of the following word.\n",
+      "\n",
+      "### Input:\n",
+      "Ocassion\n",
+      "\n",
+      "### Response:\n",
+      "The correct spelling is 'Occasion.'\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_input = format_input(data[50])\n",
+    "desired_response = f\"\\n\\n### Response:\\n{data[50]['output']}\"\n",
+    "\n",
+    "print(model_input + desired_response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a3891fa9-f738-41cd-946c-80ef9a99c346",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
+      "\n",
+      "### Instruction:\n",
+      "What is an antonym of 'complicated'?\n",
+      "\n",
+      "### Response:\n",
+      "An antonym of 'complicated' is 'simple'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_input = format_input(data[999])\n",
+    "desired_response = f\"\\n\\n### Response:\\n{data[999]['output']}\"\n",
+    "\n",
+    "print(model_input + desired_response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4aa8afd5-2a21-49a5-90c3-6a03865a4771",
+   "metadata": {},
+   "source": [
+    "- Lastly, before we prepare the PyTorch data loaders in the next section, we divide the dataset into a training, validation, and test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "aFZVopbIlNfx",
    "metadata": {
     "id": "aFZVopbIlNfx"
@@ -208,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "-zf6oht6bIUQ",
    "metadata": {
     "colab": {
@@ -222,68 +390,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "935\n",
-      "55\n",
-      "110\n"
+      "Training set length: 935\n",
+      "Validation set length: 55\n",
+      "Test set length: 110\n"
      ]
     }
    ],
    "source": [
-    "print(len(train_data))\n",
-    "print(len(val_data))\n",
-    "print(len(test_data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "Jhk37nnJnkBh",
-   "metadata": {
-    "id": "Jhk37nnJnkBh"
-   },
-   "outputs": [],
-   "source": [
-    "def format_input(entry):\n",
-    "    instruction_text = (\n",
-    "        f\"Below is an instruction that describes a task. \"\n",
-    "        f\"Write a response that appropriately completes the request.\"\n",
-    "        f\"\\n\\n### Instruction:\\n{entry['instruction']}\"\n",
-    "    )\n",
-    "\n",
-    "    input_text = f\"\\n\\n### Input:\\n{entry['input']}\" if entry[\"input\"] else \"\"\n",
-    "    instruction_text + input_text\n",
-    "\n",
-    "    return instruction_text + input_text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "F9UQRfjzo4Js",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "F9UQRfjzo4Js",
-    "outputId": "b56e6c03-f603-4e9d-c1b6-b4a70403caf9"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
-      "\n",
-      "### Instruction:\n",
-      "Evaluate the following phrase by transforming it into the spelling given.\n",
-      "\n",
-      "### Input:\n",
-      "freind --> friend\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(format_input(train_data[0]))"
+    "print(\"Training set length:\", len(train_data))\n",
+    "print(\"Validation set length:\", len(val_data))\n",
+    "print(\"Test set length:\", len(test_data))"
    ]
   },
   {
@@ -1155,7 +1271,7 @@
    "id": "87b79a47-13f9-4d1f-87b1-3339bafaf2a3",
    "metadata": {},
    "source": [
-    "## 7.6 Saving the results"
+    "## 7.6 Extracting and saving responses"
    ]
   },
   {
@@ -1659,7 +1775,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,