diff --git a/ch06/01_main-chapter-code/README.md b/ch06/01_main-chapter-code/README.md
index 6c21d9b..0e4b9fd 100644
--- a/ch06/01_main-chapter-code/README.md
+++ b/ch06/01_main-chapter-code/README.md
@@ -9,5 +9,8 @@
 
 ### Optional Code
 
+[load-finetuned-model.ipynb](load-finetuned-model.ipynb) is a standalone Jupyter notebook to load the finetuned model we created in this chapter
+
+
 - [gpt_class_finetune.py](gpt_class_finetune.py) is a standalone Python script file with the code that we implemented in [ch06.ipynb](ch06.ipynb) to finetune the GPT model (you can think of it as a chapter summary)
 
diff --git a/ch06/01_main-chapter-code/ch06.ipynb b/ch06/01_main-chapter-code/ch06.ipynb
index d784657..f4ffc34 100644
--- a/ch06/01_main-chapter-code/ch06.ipynb
+++ b/ch06/01_main-chapter-code/ch06.ipynb
@@ -2326,7 +2326,7 @@
     }
    ],
    "source": [
-    "model_state_dict = torch.load(\"review_classifier.pth\")\n",
+    "model_state_dict = torch.load(\"review_classifier.pth\", map_location=device)\n",
     "model.load_state_dict(model_state_dict)"
    ]
   },
@@ -2372,7 +2372,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
diff --git a/ch06/01_main-chapter-code/load-finetuned-model.ipynb b/ch06/01_main-chapter-code/load-finetuned-model.ipynb
new file mode 100644
index 0000000..440476f
--- /dev/null
+++ b/ch06/01_main-chapter-code/load-finetuned-model.ipynb
@@ -0,0 +1,249 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d",
+   "metadata": {},
+   "source": [
+    "<table style=\"width:100%\">\n",
+    "<tr>\n",
+    "<td style=\"vertical-align:middle; text-align:left;\">\n",
+    "<font size=\"2\">\n",
+    "Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
+    "<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
+    "</font>\n",
+    "</td>\n",
+    "<td style=\"vertical-align:middle; text-align:left;\">\n",
+    "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
+    "</td>\n",
+    "</tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3f83194-82b9-4478-9550-5ad793467bd0",
+   "metadata": {},
+   "source": [
+    "# Load And Use Finetuned Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e",
+   "metadata": {},
+   "source": [
+    "This notebook contains minimal code to load the finetuned model that was created and saved in chapter 6 via [ch06.ipynb](ch06.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tiktoken version: 0.6.0\n",
+      "torch version: 2.2.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "from importlib.metadata import version\n",
+    "\n",
+    "pkgs = [\n",
+    "    \"tiktoken\",    # Tokenizer\n",
+    "    \"torch\",       # Deep learning library\n",
+    "]\n",
+    "for p in pkgs:\n",
+    "    print(f\"{p} version: {version(p)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "finetuned_model_path = Path(\"review_classifier.pth\")\n",
+    "if not finetuned_model_path.exists():\n",
+    "    print(\n",
+    "        f\"Could not find '{finetuned_model_path}'.\\n\"\n",
+    "        \"Run the `ch06.ipynb` notebook to finetune and save the finetuned model.\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fb02584a-5e31-45d5-8377-794876907bc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from previous_chapters import GPTModel\n",
+    "\n",
+    "\n",
+    "BASE_CONFIG = {\n",
+    "    \"vocab_size\": 50257,     # Vocabulary size\n",
+    "    \"context_length\": 1024,  # Context length\n",
+    "    \"drop_rate\": 0.0,        # Dropout rate\n",
+    "    \"qkv_bias\": True         # Query-key-value bias\n",
+    "}\n",
+    "\n",
+    "model_configs = {\n",
+    "    \"gpt2-small (124M)\": {\"emb_dim\": 768, \"n_layers\": 12, \"n_heads\": 12},\n",
+    "    \"gpt2-medium (355M)\": {\"emb_dim\": 1024, \"n_layers\": 24, \"n_heads\": 16},\n",
+    "    \"gpt2-large (774M)\": {\"emb_dim\": 1280, \"n_layers\": 36, \"n_heads\": 20},\n",
+    "    \"gpt2-xl (1558M)\": {\"emb_dim\": 1600, \"n_layers\": 48, \"n_heads\": 25},\n",
+    "}\n",
+    "\n",
+    "CHOOSE_MODEL = \"gpt2-small (124M)\"\n",
+    "\n",
+    "BASE_CONFIG.update(model_configs[CHOOSE_MODEL])\n",
+    "\n",
+    "# Initialize base model\n",
+    "model_size = CHOOSE_MODEL.split(\" \")[-1].lstrip(\"(\").rstrip(\")\")\n",
+    "model = GPTModel(BASE_CONFIG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "# Convert model to classifier as in section 6.5 in ch06.ipynb\n",
+    "num_classes = 2\n",
+    "model.out_head = torch.nn.Linear(in_features=BASE_CONFIG[\"emb_dim\"], out_features=num_classes)\n",
+    "\n",
+    "# Then load pretrained weights\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model.load_state_dict(torch.load(\"review_classifier.pth\", map_location=device))\n",
+    "model.eval();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tiktoken\n",
+    "\n",
+    "tokenizer = tiktoken.get_encoding(\"gpt2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This function was implemented in ch06.ipynb\n",
+    "def classify_review(text, model, tokenizer, device, max_length=None, pad_token_id=50256):\n",
+    "    model.eval()\n",
+    "\n",
+    "    # Prepare inputs to the model\n",
+    "    input_ids = tokenizer.encode(text)\n",
+    "    supported_context_length = model.pos_emb.weight.shape[1]\n",
+    "\n",
+    "    # Truncate sequences if they too long\n",
+    "    input_ids = input_ids[:min(max_length, supported_context_length)]\n",
+    "\n",
+    "    # Pad sequences to the longest sequence\n",
+    "    input_ids += [pad_token_id] * (max_length - len(input_ids))\n",
+    "    input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0) # add batch dimension\n",
+    "\n",
+    "    # Model inference\n",
+    "    with torch.no_grad():\n",
+    "        logits = model(input_tensor)[:, -1, :]  # Logits of the last output token\n",
+    "    predicted_label = torch.argmax(logits, dim=-1).item()\n",
+    "\n",
+    "    # Return the classified result\n",
+    "    return \"spam\" if predicted_label == 1 else \"not spam\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "spam\n"
+     ]
+    }
+   ],
+   "source": [
+    "text_1 = (\n",
+    "    \"You are a winner you have been specially\"\n",
+    "    \" selected to receive $1000 cash or a $2000 award.\"\n",
+    ")\n",
+    "\n",
+    "print(classify_review(\n",
+    "    text_1, model, tokenizer, device, max_length=120\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "not spam\n"
+     ]
+    }
+   ],
+   "source": [
+    "text_2 = (\n",
+    "    \"Hey, just wanted to check if we're still on\"\n",
+    "    \" for dinner tonight? Let me know!\"\n",
+    ")\n",
+    "\n",
+    "print(classify_review(\n",
+    "    text_2, model, tokenizer, device, max_length=120\n",
+    "))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ch07/01_main-chapter-code/load-finetuned-model.ipynb b/ch07/01_main-chapter-code/load-finetuned-model.ipynb
index eaaf255..f9193fb 100644
--- a/ch07/01_main-chapter-code/load-finetuned-model.ipynb
+++ b/ch07/01_main-chapter-code/load-finetuned-model.ipynb
@@ -75,7 +75,7 @@
     "if not finetuned_model_path.exists():\n",
     "    print(\n",
     "        f\"Could not find '{finetuned_model_path}'.\\n\"\n",
-    "        \"Run the `ch07.ipynb` notebook to finetune and save finetuned model.\"\n",
+    "        \"Run the `ch07.ipynb` notebook to finetune and save the finetuned model.\"\n",
     "    )"
    ]
   },
@@ -207,7 +207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,