diff --git a/ch05/01_main-chapter-code/ch05.ipynb b/ch05/01_main-chapter-code/ch05.ipynb
index 3f9bb9e6..422058e1 100644
--- a/ch05/01_main-chapter-code/ch05.ipynb
+++ b/ch05/01_main-chapter-code/ch05.ipynb
@@ -31,10 +31,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "matplotlib version: 3.7.2\n",
-      "numpy version: 1.25.2\n",
+      "matplotlib version: 3.8.2\n",
+      "numpy version: 1.26.0\n",
       "tiktoken version: 0.5.1\n",
-      "torch version: 2.2.1\n",
+      "torch version: 2.2.2\n",
       "tensorflow version: 2.15.0\n"
      ]
     }
@@ -476,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "31402a67-a16e-4aeb-977e-70abb9c9949b",
    "metadata": {
     "colab": {
@@ -1261,34 +1261,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ep 1 (Step 000000): Train loss 9.781, Val loss 9.933\n",
-      "Ep 1 (Step 000005): Train loss 8.111, Val loss 8.339\n",
+      "Ep 1 (Step 000000): Train loss 9.689, Val loss 9.846\n",
+      "Ep 1 (Step 000005): Train loss 7.808, Val loss 8.044\n",
       "Every effort moves you,,,,,,,,,,,,.                                     \n",
-      "Ep 2 (Step 000010): Train loss 6.661, Val loss 7.048\n",
-      "Ep 2 (Step 000015): Train loss 5.961, Val loss 6.616\n",
-      "Every effort moves you, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and,, and, and,\n",
-      "Ep 3 (Step 000020): Train loss 5.726, Val loss 6.600\n",
-      "Ep 3 (Step 000025): Train loss 5.201, Val loss 6.348\n",
-      "Every effort moves you, and I had been.                                            \n",
-      "Ep 4 (Step 000030): Train loss 4.417, Val loss 6.278\n",
-      "Ep 4 (Step 000035): Train loss 4.069, Val loss 6.226\n",
-      "Every effort moves you know the                          \"I he had the donkey and I had the and I had the donkey and down the room, I had\n",
-      "Ep 5 (Step 000040): Train loss 3.732, Val loss 6.160\n",
-      "Every effort moves you know it was not that the picture--I had the fact by the last I had been--his, and in the            \"Oh, and he said, and down the room, and in\n",
-      "Ep 6 (Step 000045): Train loss 2.850, Val loss 6.179\n",
-      "Ep 6 (Step 000050): Train loss 2.427, Val loss 6.141\n",
-      "Every effort moves you know,\" was one of the picture. The--I had a little of a little: \"Yes, and in fact, and in the picture was, and I had been at my elbow and as his pictures, and down the room, I had\n",
-      "Ep 7 (Step 000055): Train loss 2.104, Val loss 6.134\n",
-      "Ep 7 (Step 000060): Train loss 1.882, Val loss 6.233\n",
-      "Every effort moves you know,\" was one of the picture for nothing--I told Mrs.  \"I was no--as! The women had been, in the moment--as Jack himself, as once one had been the donkey, and were, and in his\n",
-      "Ep 8 (Step 000065): Train loss 1.320, Val loss 6.238\n",
-      "Ep 8 (Step 000070): Train loss 0.985, Val loss 6.242\n",
-      "Every effort moves you know,\" was one of the axioms he had been the tips of a self-confident moustache, I felt to see a smile behind his close grayish beard--as if he had the donkey. \"strongest,\" as his\n",
-      "Ep 9 (Step 000075): Train loss 0.717, Val loss 6.293\n",
-      "Ep 9 (Step 000080): Train loss 0.541, Val loss 6.393\n",
-      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back the window-curtains, I had the donkey. \"There were days when I\n",
-      "Ep 10 (Step 000085): Train loss 0.391, Val loss 6.452\n",
-      "Every effort moves you know,\" was one of the axioms he laid down across the Sevres and silver of an exquisitely appointed luncheon-table, when, on a later day, I had again run over from Monte Carlo; and Mrs. Gis\n"
+      "Ep 2 (Step 000010): Train loss 6.380, Val loss 6.800\n",
+      "Ep 2 (Step 000015): Train loss 5.877, Val loss 6.592\n",
+      "Every effort moves you, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and\n",
+      "Ep 3 (Step 000020): Train loss 16.090, Val loss 16.154\n",
+      "Ep 3 (Step 000025): Train loss 5.440, Val loss 6.384\n",
+      "Every effort moves you of the                                                \n",
+      "Ep 4 (Step 000030): Train loss 4.796, Val loss 6.328\n",
+      "Ep 4 (Step 000035): Train loss 4.215, Val loss 6.258\n",
+      "Every effort moves you know it                                                \n",
+      "Ep 5 (Step 000040): Train loss 3.825, Val loss 6.221\n",
+      "Every effort moves you know the \"Oh, and in the fact, and I had been him.     \"Oh, and in the first I, and I had been the donkey. \"Oh, and in the first, and in the\n",
+      "Ep 6 (Step 000045): Train loss 2.906, Val loss 6.221\n",
+      "Ep 6 (Step 000050): Train loss 2.481, Val loss 6.194\n",
+      "Every effort moves you know the \"I turned, I was not the fact with a little a little: \"Yes, and in fact, and I had been the moment--as Jack himself, and in the fact, I had been the picture--I had the\n",
+      "Ep 7 (Step 000055): Train loss 2.169, Val loss 6.162\n",
+      "Ep 7 (Step 000060): Train loss 1.843, Val loss 6.274\n",
+      "Every effort moves you know,\" was one of the picture for nothing--I told Mrs.  \"I looked--I looked up, I felt to see a smile behind his close that he had been; and as I felt, and down, with a small picture\n",
+      "Ep 8 (Step 000065): Train loss 1.244, Val loss 6.259\n",
+      "Ep 8 (Step 000070): Train loss 0.923, Val loss 6.272\n",
+      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back his head to look up at the honour being _mine_--because he had always his\n",
+      "Ep 9 (Step 000075): Train loss 0.649, Val loss 6.329\n",
+      "Ep 9 (Step 000080): Train loss 0.477, Val loss 6.462\n",
+      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back the window-curtains, I saw that, and down the room, my eyes\n",
+      "Ep 10 (Step 000085): Train loss 0.335, Val loss 6.542\n",
+      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back his head to look up at the sketch of the donkey. \"There were days when I\n"
      ]
     }
    ],
@@ -1321,7 +1321,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 500x300 with 2 Axes>"
       ]
@@ -1425,7 +1425,11 @@
      "output_type": "stream",
      "text": [
       "Output text:\n",
-      " Every effort moves you know,\" was one of the axioms he laid down across the Sevres and silver of an exquisitely appointed lun\n"
+      " Every effort moves you?\"\n",
+      "\n",
+      "\"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"\n",
+      "\n",
+      "\n"
      ]
     }
    ],
@@ -1621,7 +1625,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x300 with 1 Axes>"
       ]
@@ -1884,7 +1888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 41,
    "id": "aa2a0d7d-0457-42d1-ab9d-bd67683e7ed8",
    "metadata": {},
    "outputs": [
@@ -1893,7 +1897,8 @@
      "output_type": "stream",
      "text": [
       "Output text:\n",
-      " Every effort moves you stand to work on surprise, a one of us had gone with random-\n"
+      " Every effort moves youlit,\" was one of the axioms he laid down across the\n",
+      "\n"
      ]
     }
    ],
@@ -1940,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 42,
    "id": "3d67d869-ac04-4382-bcfb-c96d1ca80d47",
    "metadata": {},
    "outputs": [],
@@ -1958,7 +1963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 43,
    "id": "9d57d914-60a3-47f1-b499-5352f4c457cb",
    "metadata": {},
    "outputs": [],
@@ -1979,7 +1984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 44,
    "id": "bbd175bb-edf4-450e-a6de-d3e8913c6532",
    "metadata": {},
    "outputs": [],
@@ -1994,7 +1999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 45,
    "id": "8a0c7295-c822-43bf-9286-c45abc542868",
    "metadata": {},
    "outputs": [],
@@ -2047,7 +2052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 46,
    "id": "fb9fdf02-972a-444e-bf65-8ffcaaf30ce8",
    "metadata": {},
    "outputs": [],
@@ -2057,7 +2062,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 47,
    "id": "a0747edc-559c-44ef-a93f-079d60227e3f",
    "metadata": {},
    "outputs": [
@@ -2066,7 +2071,7 @@
      "output_type": "stream",
      "text": [
       "TensorFlow version: 2.15.0\n",
-      "tqdm version: 4.66.2\n"
+      "tqdm version: 4.66.1\n"
      ]
     }
    ],
@@ -2077,7 +2082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 48,
    "id": "c5bc89eb-4d39-4287-9b0c-e459ebe7f5ed",
    "metadata": {},
    "outputs": [],
@@ -2096,7 +2101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 49,
    "id": "76271dd7-108d-4f5b-9c01-6ae0aac4b395",
    "metadata": {},
    "outputs": [
@@ -2106,7 +2111,7 @@
      "text": [
       "File already exists and is up-to-date: gpt2/124M/checkpoint\n",
       "File already exists and is up-to-date: gpt2/124M/encoder.json\n",
-      "File already exists and is up-to-date: gpt2/124M/settings.json\n",
+      "File already exists and is up-to-date: gpt2/124M/hparams.json\n",
       "File already exists and is up-to-date: gpt2/124M/model.ckpt.data-00000-of-00001\n",
       "File already exists and is up-to-date: gpt2/124M/model.ckpt.index\n",
       "File already exists and is up-to-date: gpt2/124M/model.ckpt.meta\n",
@@ -2120,7 +2125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 50,
    "id": "b1a31951-d971-4a6e-9c43-11ee1168ec6a",
    "metadata": {},
    "outputs": [
@@ -2138,7 +2143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 51,
    "id": "857c8331-130e-46ba-921d-fa35d7a73cfe",
    "metadata": {},
    "outputs": [
@@ -2154,6 +2159,38 @@
     "print(\"Parameter dictionary keys:\", params.keys())"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "c48dac94-8562-4a66-84ef-46c613cdc4cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.11010301 -0.03926672  0.03310751 ... -0.1363697   0.01506208\n",
+      "   0.04531523]\n",
+      " [ 0.04034033 -0.04861503  0.04624869 ...  0.08605453  0.00253983\n",
+      "   0.04318958]\n",
+      " [-0.12746179  0.04793796  0.18410145 ...  0.08991534 -0.12972379\n",
+      "  -0.08785918]\n",
+      " ...\n",
+      " [-0.04453601 -0.05483596  0.01225674 ...  0.10435229  0.09783269\n",
+      "  -0.06952604]\n",
+      " [ 0.1860082   0.01665728  0.04611587 ... -0.09625227  0.07847701\n",
+      "  -0.02245961]\n",
+      " [ 0.05135201 -0.02768905  0.0499369  ...  0.00704835  0.15519823\n",
+      "   0.12067825]]\n",
+      "Token embedding weight tensor dimensions: (50257, 768)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(params[\"wte\"])\n",
+    "print(\"Token embedding weight tensor dimensions:\", params[\"wte\"].shape)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "466e100c-294e-4afc-a70a-2f398ac4c104",
@@ -2184,7 +2221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 53,
    "id": "9fef90dd-0654-4667-844f-08e28339ef7d",
    "metadata": {},
    "outputs": [],
@@ -2217,7 +2254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 54,
    "id": "f9a92229-c002-49a6-8cfb-248297ad8296",
    "metadata": {},
    "outputs": [],
@@ -2230,7 +2267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 55,
    "id": "f22d5d95-ca5a-425c-a9ec-fc432a12d4e9",
    "metadata": {},
    "outputs": [],
@@ -2312,7 +2349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 56,
    "id": "1f690253-f845-4347-b7b6-43fabbd2affa",
    "metadata": {},
    "outputs": [
diff --git a/ch05/01_main-chapter-code/gpt_download.py b/ch05/01_main-chapter-code/gpt_download.py
index 690d2cee..786620c4 100644
--- a/ch05/01_main-chapter-code/gpt_download.py
+++ b/ch05/01_main-chapter-code/gpt_download.py
@@ -16,7 +16,7 @@ def download_and_load_gpt2(model_size, models_dir):
     model_dir = os.path.join(models_dir, model_size)
     base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
     filenames = [
-        "checkpoint", "encoder.json", "settings.json",
+        "checkpoint", "encoder.json", "hparams.json",
         "model.ckpt.data-00000-of-00001", "model.ckpt.index",
         "model.ckpt.meta", "vocab.bpe"
     ]
@@ -30,7 +30,7 @@ def download_and_load_gpt2(model_size, models_dir):
 
     # Load settings and params
     tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
-    settings = json.load(open(os.path.join(model_dir, "settings.json")))
+    settings = json.load(open(os.path.join(model_dir, "hparams.json")))
     params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, settings)
 
     return settings, params
diff --git a/ch05/01_main-chapter-code/gpt_generate.py b/ch05/01_main-chapter-code/gpt_generate.py
index 948dcff9..c8f54aa9 100644
--- a/ch05/01_main-chapter-code/gpt_generate.py
+++ b/ch05/01_main-chapter-code/gpt_generate.py
@@ -37,7 +37,7 @@ def download_and_load_gpt2(model_size, models_dir):
     model_dir = os.path.join(models_dir, model_size)
     base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
     filenames = [
-        "checkpoint", "encoder.json", "settings.json",
+        "checkpoint", "encoder.json", "hparams.json",
         "model.ckpt.data-00000-of-00001", "model.ckpt.index",
         "model.ckpt.meta", "vocab.bpe"
     ]
@@ -51,7 +51,7 @@ def download_and_load_gpt2(model_size, models_dir):
 
     # Load settings and params
     tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
-    settings = json.load(open(os.path.join(model_dir, "settings.json")))
+    settings = json.load(open(os.path.join(model_dir, "hparams.json")))
     params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, settings)
 
     return settings, params