diff --git a/ch05/07_gpt_to_llama/converting-llama2-to-llama3.ipynb b/ch05/07_gpt_to_llama/converting-llama2-to-llama3.ipynb
index 9a69c5d..0f13d64 100644
--- a/ch05/07_gpt_to_llama/converting-llama2-to-llama3.ipynb
+++ b/ch05/07_gpt_to_llama/converting-llama2-to-llama3.ipynb
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "ws0wsUzwLH2k",
    "metadata": {
     "id": "ws0wsUzwLH2k"
@@ -87,7 +87,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "34a9a440-84c2-42cc-808b-38677cb6af8a",
-    "outputId": "1a64035b-daeb-4514-a49f-6bfde84357e7"
+    "outputId": "e3d3d4b6-ee63-4e28-d794-e8b0bdd931fd"
    },
    "outputs": [
     {
@@ -561,7 +561,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "b4b8f085-349e-4674-a3f0-78fde0664fac",
-    "outputId": "16b37235-d4a0-41ac-b878-f0d2f9584174"
+    "outputId": "9da09d72-43b1-45af-d46f-6928ea4af33a"
    },
    "outputs": [
     {
@@ -618,7 +618,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "15e65d3c-7b42-4ed3-bfee-bb09578657bb",
-    "outputId": "85432dba-827b-4a27-aedd-63f9c5044352"
+    "outputId": "69709a78-2aaa-4597-8142-2f44eb59753f"
    },
    "outputs": [
     {
@@ -668,7 +668,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "58f713aa-ac00-4e2f-8247-94609aa01350",
-    "outputId": "20caa098-41bd-4572-e8b7-1020073c5912"
+    "outputId": "486dfd9c-9f3a-4b9e-f9a2-35fb43b9a5fb"
    },
    "outputs": [
     {
@@ -917,7 +917,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "6079f747-8f20-4c6b-8d38-7156f1101729",
-    "outputId": "8ce6476f-ea77-4513-d31a-3d3cdffa3044"
+    "outputId": "0a8cd23b-d9fa-4c2d-ca63-3fc79bc4de0d"
    },
    "outputs": [
     {
@@ -953,7 +953,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "0df1c79e-27a7-4b0f-ba4e-167fe107125a",
-    "outputId": "13683a74-017a-41d1-a49f-c264f899c4cc"
+    "outputId": "3425e9ce-d8c0-4b37-bded-a2c60b66a41a"
    },
    "outputs": [
     {
@@ -1143,7 +1143,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "3357a230-b678-4691-a238-257ee4e80185",
-    "outputId": "57472e6e-95c4-4b41-bc18-231f6ff69e95"
+    "outputId": "a3652def-ea7f-46fb-f293-2a59affb71a0"
    },
    "outputs": [
     {
@@ -1184,24 +1184,10 @@
    "id": "69714ea8-b9b8-4687-8392-f3abb8f93a32",
    "metadata": {
     "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 153,
-     "referenced_widgets": [
-      "dd1779b5e0484f0c9c72af34a6a3e638",
-      "2dc88f14cf83432fbfb62c914a40a9d3",
-      "f42effc8bf4b443eba7d108b69d4d417",
-      "3ea3c0f23f1746ce82685c92056ee83d",
-      "cc6d7bc9b1034e208d14ef0a2e2766cd",
-      "0f6cd37c1bf14d32922d1f24fe57f895",
-      "5cad22d53fe34dc4af4d6a2bcc0f3081",
-      "80615905cbd8495dbe72924048de5fec",
-      "9c4420d3100440f1bf217d30b5ef74c5",
-      "e98c47789e4d43e9950f6c496dc8ccea",
-      "0f61d20b92c54ce2843790b4acbd49b5"
-     ]
+     "base_uri": "https://localhost:8080/"
     },
     "id": "69714ea8-b9b8-4687-8392-f3abb8f93a32",
-    "outputId": "13ad9048-e4b3-4232-f2e3-ce9546738af0"
+    "outputId": "c9836ba8-5176-4dd5-b618-6cc36fdbe1f0"
    },
    "outputs": [
     {
@@ -1215,20 +1201,6 @@
       "Please note that authentication is recommended but still optional to access public models or datasets.\n",
       "  warnings.warn(\n"
      ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dd1779b5e0484f0c9c72af34a6a3e638",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.model:   0%|          | 0.00/2.18M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1254,7 +1226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "5dm6Oz7uAytV",
    "metadata": {
     "id": "5dm6Oz7uAytV"
@@ -1295,7 +1267,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "e0a2b5cd-6cba-4d72-b8ff-04d8315d483e",
-    "outputId": "cf4637be-525c-432e-eaf8-a92e10242d2b"
+    "outputId": "990d7b74-cb35-476b-d8bd-d544006e00f4"
    },
    "outputs": [
     {
@@ -1369,60 +1341,60 @@
      "base_uri": "https://localhost:8080/",
      "height": 145,
      "referenced_widgets": [
-      "bf1ddeaf6985478ca466b71e4724a0f9",
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      ]
     },
     "id": "5fa9c06c-7a53-4b4d-9ce4-acc027322ee4",
-    "outputId": "3b5c3da6-8fe3-4654-e8bb-f8b55014a1e8"
+    "outputId": "c05118ce-9f81-41c8-a1f2-72caa932ae86"
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bf1ddeaf6985478ca466b71e4724a0f9",
+       "model_id": "f3788acce34f4956b0727b58d0cf38c6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1436,7 +1408,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "058766ddf03a4148a685170ccaea1831",
+       "model_id": "8ae98969541849efa356cf912ac39b1e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1450,7 +1422,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6a1b639c131a4f3383b33d7f542b558d",
+       "model_id": "9bb60a5a3710463ebe3a17f8d2a446be",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1464,7 +1436,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aaecfb3b66644f0982be5fb4d27dd484",
+       "model_id": "ed28e180d94a4b7aa548581612e31232",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1510,7 +1482,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "ee26bd0b-fea9-4924-97f7-409c14f28e49",
-    "outputId": "18fee047-bd63-48b2-f1e1-bf6dd3eebf66"
+    "outputId": "2fbc2786-677f-4fea-9472-5fb8542ff14b"
    },
    "outputs": [
     {
@@ -1632,6 +1604,7 @@
     "        model.out_head.weight = assign(model.out_head.weight, params[\"lm_head.weight\"], \"lm_head.weight\")\n",
     "    else:\n",
     "        model.out_head.weight = assign(model.out_head.weight, params[\"model.embed_tokens.weight\"], \"model.embed_tokens.weight\")\n",
+    "        print(\"Model uses weight tying.\")\n",
     "\n",
     "\n",
     "load_weights_into_llama(model, LLAMA3_CONFIG_8B, combined_weights)\n",
@@ -1658,7 +1631,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "240987e8-a023-462e-9376-9edfb27559ec",
-    "outputId": "1750847d-f7b5-4065-c0ba-5a66de2f55af"
+    "outputId": "6dab0e56-40a8-45db-a096-ab2b9ee97a69"
    },
    "outputs": [
     {
@@ -1736,60 +1709,60 @@
      "base_uri": "https://localhost:8080/",
      "height": 145,
      "referenced_widgets": [
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+      "e0550cab24c7492787af40dc4b8576bf",
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      ]
     },
     "id": "nbvAV7vaz6yc",
-    "outputId": "6b3cbcc1-befc-4a54-e4e4-098a77f2bbf9"
+    "outputId": "9e1badc9-a6c4-48b7-9125-e0810655528b"
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2c872665b8544c3bee203e9e60eb6dd",
+       "model_id": "409470784b6346a981920350de4f6f28",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1803,7 +1776,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "55db14a6cb024cfea22c78053df35c0a",
+       "model_id": "d598f094c3ce4daeab19fac8094cba7e",
        "version_major": 2,
        "version_minor": 0
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@@ -1817,7 +1790,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7ef7486e428246a19c59e787fa6f1415",
+       "model_id": "98b4680141ee423bb5e43c47613d8440",
        "version_major": 2,
        "version_minor": 0
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@@ -1831,7 +1804,7 @@
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      "data": {
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-       "model_id": "dc327439174446bc967c2db256d16cb3",
+       "model_id": "05b502e1e3a9436297dafbb1ce7af722",
        "version_major": 2,
        "version_minor": 0
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@@ -1933,7 +1906,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "nwBrTGTsUNhn",
-    "outputId": "587b5259-3124-46de-b13f-ef2d1662026d"
+    "outputId": "72a495b4-b872-429a-88ef-49a9b4577f0f"
    },
    "outputs": [
     {
@@ -1959,7 +1932,7 @@
      "height": 36
     },
     "id": "0fpmpVgYVTRZ",
-    "outputId": "fb24c3f2-da13-4429-e5c3-13e016d51eac"
+    "outputId": "bb3e819a-112a-466c-ac51-5d14a9c3475b"
    },
    "outputs": [
     {
@@ -1999,7 +1972,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "ozGOBu6XOkEW",
-    "outputId": "bae98ed7-0e6b-439d-978e-9c48710b7e25"
+    "outputId": "4f689c70-bed9-46f3-a52a-aea47b641283"
    },
    "outputs": [
     {
@@ -2186,7 +2159,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "a7l21VE4Otcs",
-    "outputId": "ea31712b-6678-4ad3-d246-a7e1ad7cbd66"
+    "outputId": "3dd5cfba-bf3f-44d2-9be1-7cd42bfe4ba9"
    },
    "outputs": [
     {
@@ -2213,60 +2186,60 @@
      "base_uri": "https://localhost:8080/",
      "height": 145,
      "referenced_widgets": [
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     "id": "u4J7IxOvOyPM",
-    "outputId": "8ea92361-66a9-4c45-f1c4-cdc7d322f0ba"
+    "outputId": "925348d7-fc69-4d1b-90f1-7029426bcfcf"
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    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f5c112068f5a4850b6291fe85fa6ad7d",
+       "model_id": "5bbaa046d8934c8fae0a12c3d7bd991b",
        "version_major": 2,
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@@ -2280,7 +2253,7 @@
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-       "model_id": "05dd05934e164c159bd73cc015ba2d39",
+       "model_id": "af985cf6fa26475eb2c4dd81e0c79ff4",
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@@ -2294,7 +2267,7 @@
     {
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-       "model_id": "7c55b07c8efa4c2a97ea201f09fbeb56",
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@@ -2308,7 +2281,7 @@
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-       "model_id": "3ec5cf160bd146f397a34d72008efabf",
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@@ -2345,7 +2318,7 @@
      "base_uri": "https://localhost:8080/"
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     "id": "wJFnF8ATPbtD",
-    "outputId": "b4e82f13-c8e1-4d49-d27d-6b96b1eebc17"
+    "outputId": "67d5cb66-3588-4fd4-ac75-39bfe3aa82d8"
    },
    "outputs": [
     {
@@ -2392,6 +2365,7 @@
    "source": [
     "- As of this writing, Meta AI's latest models are the Llama 3.2 models announced [here](https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/)\n",
     "- The code for the Llama 3.2 text model is similar to that of Llama 3.1, except that the model has shrunk in size (there is a 1B and 3B version)\n",
+    "- The other efficiency tweak was that they added back weight tying (a concept that was original used in the GPT-2 architecture); here, they reuse the same weight parameter values in the input (token) embedding layer and output layer\n",
     "- The small model size of Llama 3.2 1B is quite convenient, since it can even run on many mobile devices\n",
     "- The architectural differences between Llama 3.1 8B and Llama 3.2 1B are illustrated in the figure below"
    ]
@@ -2403,7 +2377,7 @@
     "id": "OL1EoXQ6TPb7"
    },
    "source": [
-    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/llama31-to-llama32.webp\" width=\"700px\">"
+    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/llama31-to-llama32.webp?1\" width=\"700px\">"
    ]
   },
   {
@@ -2500,42 +2474,9 @@
    "execution_count": 45,
    "id": "jt8BKAHXRCPI",
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 49,
-     "referenced_widgets": [
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-    "id": "jt8BKAHXRCPI",
-    "outputId": "2a95b5da-69f5-40ba-c180-27c8150af310"
+    "id": "jt8BKAHXRCPI"
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "db0b4112ca1c4070b6c08ae77198a07b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.model:   0%|          | 0.00/2.18M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tokenizer_file_path = hf_hub_download(\n",
     "    repo_id=\"meta-llama/Llama-3.2-1B\",\n",
@@ -2548,21 +2489,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 50,
    "id": "uf8KjasmRFSt",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "uf8KjasmRFSt",
-    "outputId": "8611df79-4325-4412-d191-37c8b1688bea"
+    "outputId": "4e718852-2aa1-4b5a-bec3-3d5f866a4038"
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total number of parameters: 1,498,482,688\n"
+      "Total number of parameters: 1,498,482,688\n",
+      "\n",
+      "Total number of unique parameters: 1,235,814,400\n"
      ]
     }
    ],
@@ -2570,7 +2513,11 @@
     "model = Llama3Model(LLAMA32_CONFIG_1B)\n",
     "\n",
     "total_params = sum(p.numel() for p in model.parameters())\n",
-    "print(f\"Total number of parameters: {total_params:,}\")"
+    "print(f\"Total number of parameters: {total_params:,}\")\n",
+    "\n",
+    "# Account for weight tying\n",
+    "total_params_normalized = total_params - model.tok_emb.weight.numel()\n",
+    "print(f\"\\nTotal number of unique parameters: {total_params_normalized:,}\")"
    ]
   },
   {
@@ -2579,39 +2526,18 @@
    "id": "9FbCIYW7RIOe",
    "metadata": {
     "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 49,
-     "referenced_widgets": [
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-     ]
+     "base_uri": "https://localhost:8080/"
     },
     "id": "9FbCIYW7RIOe",
-    "outputId": "01baf122-a69e-493a-99ed-39056347af51"
+    "outputId": "35588405-e2e1-4871-a1db-1d4bcb852e49"
    },
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ccfdc4a7fa274eabaaefa64867ace0d6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors:   0%|          | 0.00/2.47G [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model uses weight tying.\n"
+     ]
     }
    ],
    "source": [
@@ -2629,13 +2555,37 @@
   {
    "cell_type": "code",
    "execution_count": 48,
+   "id": "pPp5yjir6FYJ",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pPp5yjir6FYJ",
+    "outputId": "6c8e79d2-0769-43a7-93b3-f04c030e1aac"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Weight tying: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Weight tying:\", torch.equal(model.tok_emb.weight, model.out_head.weight))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
    "id": "3kh7yrw2W4qr",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "3kh7yrw2W4qr",
-    "outputId": "60fb608d-2903-4f1f-b7af-037d9a530d11"
+    "outputId": "b7e66a17-57ec-4b0e-c4ff-8d9a6b8e6ea5"
    },
    "outputs": [
     {
@@ -2710,43 +2660,163 @@
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diff --git a/ch05/07_gpt_to_llama/standalone-llama32.ipynb b/ch05/07_gpt_to_llama/standalone-llama32.ipynb
index f760eed..ff743dd 100644
--- a/ch05/07_gpt_to_llama/standalone-llama32.ipynb
+++ b/ch05/07_gpt_to_llama/standalone-llama32.ipynb
@@ -445,13 +445,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total number of parameters: 1,498,482,688\n"
+      "Total number of parameters: 1,498,482,688\n",
+      "\n",
+      "Total number of unique parameters: 1,235,814,400\n"
      ]
     }
    ],
    "source": [
     "total_params = sum(p.numel() for p in model.parameters())\n",
-    "print(f\"Total number of parameters: {total_params:,}\")"
+    "print(f\"Total number of parameters: {total_params:,}\")\n",
+    "\n",
+    "# Account for weight tying\n",
+    "total_params_normalized = total_params - model.tok_emb.weight.numel()\n",
+    "print(f\"\\nTotal number of unique parameters: {total_params_normalized:,}\")"
    ]
   },
   {
@@ -627,7 +633,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "edcc384a-adb7-43f6-acc3-ebe4b182ec91",
+   "id": "e9d96dc8-603a-4cb5-8c3e-4d2ca56862ed",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -749,7 +755,8 @@
     "    if \"lm_head.weight\" in params.keys():\n",
     "        model.out_head.weight = assign(model.out_head.weight, params[\"lm_head.weight\"], \"lm_head.weight\")\n",
     "    else:\n",
-    "        model.out_head.weight = assign(model.out_head.weight, params[\"model.embed_tokens.weight\"], \"model.embed_tokens.weight\")"
+    "        model.out_head.weight = assign(model.out_head.weight, params[\"model.embed_tokens.weight\"], \"model.embed_tokens.weight\")\n",
+    "        print(\"Model uses weight tying.\")"
    ]
   },
   {
@@ -757,7 +764,15 @@
    "execution_count": 18,
    "id": "699cb1b8-a67d-49fb-80a6-0dad9d81f392",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model uses weight tying.\n"
+     ]
+    }
+   ],
    "source": [
     "from safetensors.torch import load_file\n",
     "\n",
@@ -773,7 +788,7 @@
     "\n",
     "else:\n",
     "    combined_weights = {}\n",
-    "    for i in range(1, 5):\n",
+    "    for i in range(1, 3):\n",
     "        weights_file = hf_hub_download(\n",
     "            repo_id=f\"meta-llama/Llama-3.2-{LLAMA_SIZE_STR}-Instruct\",\n",
     "            filename=f\"model-0000{i}-of-00002.safetensors\",\n",
@@ -787,6 +802,24 @@
     "model.to(device);"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7f9f7ccc-70cb-41ff-9c25-44336042fc37",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Weight tying: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Weight tying:\", torch.equal(model.tok_emb.weight, model.out_head.weight))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "57d07df1-4401-4792-b549-7c4cc5632323",
@@ -798,7 +831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "7b8401c6-e244-4cb7-9849-2ba71ce758d5",
    "metadata": {},
    "outputs": [],
@@ -855,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "1c7a04fa-6aac-416b-8f63-f1e19227633d",
    "metadata": {},
    "outputs": [
@@ -934,14 +967,6 @@
     "\n",
     "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf864c28-2ce1-44bf-84e4-c0671f494d62",
-   "metadata": {},
-   "outputs": [],
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   }
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