diff --git a/.gitignore b/.gitignore
index fc5f24d..1d2cf9e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,7 +14,8 @@ ch05/01_main-chapter-code/model.pth
 ch05/01_main-chapter-code/model_and_optimizer.pth
 ch05/03_bonus_pretraining_on_gutenberg/model_checkpoints
 
-# Preprocessing output folders
+# Datasets
+ch05/03_bonus_pretraining_on_gutenberg/gutenberg
 ch05/03_bonus_pretraining_on_gutenberg/gutenberg_preprocessed
 
 # Temporary OS-related files
diff --git a/appendix-D/01_main-chapter-code/appendix-D.ipynb b/appendix-D/01_main-chapter-code/appendix-D.ipynb
index 69dbdab..95bca82 100644
--- a/appendix-D/01_main-chapter-code/appendix-D.ipynb
+++ b/appendix-D/01_main-chapter-code/appendix-D.ipynb
@@ -47,7 +47,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch version: 2.2.1\n"
+      "torch version: 2.2.2\n"
      ]
     }
    ],
@@ -130,7 +130,8 @@
     "    max_length=GPT_CONFIG_124M[\"context_length\"],\n",
     "    stride=GPT_CONFIG_124M[\"context_length\"],\n",
     "    drop_last=True,\n",
-    "    shuffle=True\n",
+    "    shuffle=True,\n",
+    "    num_workers=0\n",
     ")\n",
     "\n",
     "val_loader = create_dataloader_v1(\n",
@@ -139,7 +140,8 @@
     "    max_length=GPT_CONFIG_124M[\"context_length\"],\n",
     "    stride=GPT_CONFIG_124M[\"context_length\"],\n",
     "    drop_last=False,\n",
-    "    shuffle=False\n",
+    "    shuffle=False,\n",
+    "    num_workers=0\n",
     ")"
    ]
   },
@@ -500,7 +502,7 @@
     "\n",
     "\n",
     "def train_model(model, train_loader, val_loader, optimizer, device, n_epochs,\n",
-    "                eval_freq, eval_iter, start_context, warmup_steps=10,\n",
+    "                eval_freq, eval_iter, start_context, tokenizer, warmup_steps=10,\n",
     "                initial_lr=3e-05, min_lr=1e-6):\n",
     "\n",
     "    train_losses, val_losses, track_tokens_seen, track_lrs = [], [], [], []\n",
@@ -562,8 +564,7 @@
     "\n",
     "        # Generate and print a sample from the model to monitor progress\n",
     "        generate_and_print_sample(\n",
-    "            model, train_loader.dataset.tokenizer,\n",
-    "            device, start_context\n",
+    "            model, tokenizer, device, start_context\n",
     "        )\n",
     "\n",
     "    return train_losses, val_losses, track_tokens_seen, track_lrs"
@@ -625,18 +626,21 @@
     }
    ],
    "source": [
+    "import tiktoken\n",
+    "\n",
     "torch.manual_seed(123)\n",
     "model = GPTModel(GPT_CONFIG_124M)\n",
     "model.to(device)\n",
     "\n",
     "peak_lr = 5e-4\n",
     "optimizer = torch.optim.AdamW(model.parameters(), weight_decay=0.1)\n",
+    "tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
     "\n",
     "n_epochs = 15\n",
     "train_losses, val_losses, tokens_seen, lrs = train_model(\n",
     "    model, train_loader, val_loader, optimizer, device, n_epochs=n_epochs,\n",
     "    eval_freq=5, eval_iter=1, start_context=\"Every effort moves you\",\n",
-    "    warmup_steps=10, initial_lr=1e-5, min_lr=1e-5\n",
+    "    tokenizer=tokenizer, warmup_steps=10, initial_lr=1e-5, min_lr=1e-5\n",
     ")"
    ]
   },
@@ -705,7 +709,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/jg/tpqyh1fd5js5wsr1d138k3n40000gn/T/ipykernel_34986/3589549395.py:5: UserWarning: The figure layout has changed to tight\n",
+      "/var/folders/jg/tpqyh1fd5js5wsr1d138k3n40000gn/T/ipykernel_9436/3589549395.py:5: UserWarning: The figure layout has changed to tight\n",
       "  plt.tight_layout(); plt.savefig(\"3.pdf\")\n"
      ]
     },
@@ -755,7 +759,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/appendix-D/01_main-chapter-code/previous_chapters.py b/appendix-D/01_main-chapter-code/previous_chapters.py
index 9c2fdfa..275eac9 100644
--- a/appendix-D/01_main-chapter-code/previous_chapters.py
+++ b/appendix-D/01_main-chapter-code/previous_chapters.py
@@ -20,12 +20,11 @@ import matplotlib.pyplot as plt
 
 class GPTDatasetV1(Dataset):
     def __init__(self, txt, tokenizer, max_length, stride):
-        self.tokenizer = tokenizer
         self.input_ids = []
         self.target_ids = []
 
         # Tokenize the entire text
-        token_ids = self.tokenizer.encode(txt)
+        token_ids = tokenizer.encode(txt)
 
         # Use a sliding window to chunk the book into overlapping sequences of max_length
         for i in range(0, len(token_ids) - max_length, stride):
@@ -42,7 +41,7 @@ class GPTDatasetV1(Dataset):
 
 
 def create_dataloader_v1(txt, batch_size=4, max_length=256,
-                         stride=128, shuffle=True, drop_last=True):
+                         stride=128, shuffle=True, drop_last=True, num_workers=0):
     # Initialize the tokenizer
     tokenizer = tiktoken.get_encoding("gpt2")
 
@@ -51,7 +50,7 @@ def create_dataloader_v1(txt, batch_size=4, max_length=256,
 
     # Create dataloader
     dataloader = DataLoader(
-        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)
+        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=0)
 
     return dataloader
 
diff --git a/ch02/01_main-chapter-code/ch02.ipynb b/ch02/01_main-chapter-code/ch02.ipynb
index b639fa8..8ac329c 100644
--- a/ch02/01_main-chapter-code/ch02.ipynb
+++ b/ch02/01_main-chapter-code/ch02.ipynb
@@ -37,7 +37,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch version: 2.2.1\n",
+      "torch version: 2.2.2\n",
       "tiktoken version: 0.5.1\n"
      ]
     }
@@ -724,7 +724,7 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
       "Cell \u001b[0;32mIn[16], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m SimpleTokenizerV1(vocab)\n\u001b[1;32m      3\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHello, do you like tea. Is this-- a test?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m \u001b[43mtokenizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[12], line 9\u001b[0m, in \u001b[0;36mSimpleTokenizerV1.encode\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m      7\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m([,.?_!\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m()\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m]|--|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms)\u001b[39m\u001b[38;5;124m'\u001b[39m, text)\n\u001b[1;32m      8\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m [item\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m preprocessed \u001b[38;5;28;01mif\u001b[39;00m item\u001b[38;5;241m.\u001b[39mstrip()]\n\u001b[0;32m----> 9\u001b[0m ids \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstr_to_int[s] \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m preprocessed]\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ids\n",
+      "Cell \u001b[0;32mIn[12], line 9\u001b[0m, in \u001b[0;36mSimpleTokenizerV1.encode\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m      7\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m([,.?_!\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m()\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m]|--|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms)\u001b[39m\u001b[38;5;124m'\u001b[39m, text)\n\u001b[1;32m      8\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m [item\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m preprocessed \u001b[38;5;28;01mif\u001b[39;00m item\u001b[38;5;241m.\u001b[39mstrip()]\n\u001b[0;32m----> 9\u001b[0m ids \u001b[38;5;241m=\u001b[39m \u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstr_to_int\u001b[49m\u001b[43m[\u001b[49m\u001b[43ms\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpreprocessed\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ids\n",
       "Cell \u001b[0;32mIn[12], line 9\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      7\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m([,.?_!\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m()\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m]|--|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms)\u001b[39m\u001b[38;5;124m'\u001b[39m, text)\n\u001b[1;32m      8\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m [item\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m preprocessed \u001b[38;5;28;01mif\u001b[39;00m item\u001b[38;5;241m.\u001b[39mstrip()]\n\u001b[0;32m----> 9\u001b[0m ids \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstr_to_int\u001b[49m\u001b[43m[\u001b[49m\u001b[43ms\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m preprocessed]\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ids\n",
       "\u001b[0;31mKeyError\u001b[0m: 'Hello'"
      ]
@@ -957,7 +957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "id": "ede1d41f-934b-4bf4-8184-54394a257a94",
    "metadata": {},
    "outputs": [],
@@ -967,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 25,
    "id": "48967a77-7d17-42bf-9e92-fc619d63a59e",
    "metadata": {},
    "outputs": [
@@ -988,7 +988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 26,
    "id": "6ad3312f-a5f7-4efc-9d7d-8ea09d7b5128",
    "metadata": {},
    "outputs": [],
@@ -998,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "id": "5ff2cd85-7cfb-4325-b390-219938589428",
    "metadata": {},
    "outputs": [
@@ -1020,7 +1020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 28,
    "id": "d26a48bb-f82e-41a8-a955-a1c9cf9d50ab",
    "metadata": {},
    "outputs": [
@@ -1080,7 +1080,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 29,
    "id": "848d5ade-fd1f-46c3-9e31-1426e315c71b",
    "metadata": {},
    "outputs": [
@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 30,
    "id": "e84424a7-646d-45b6-99e3-80d15fb761f2",
    "metadata": {},
    "outputs": [],
@@ -1121,7 +1121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 31,
    "id": "dfbff852-a92f-48c8-a46d-143a0f109f40",
    "metadata": {},
    "outputs": [
@@ -1154,7 +1154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 32,
    "id": "d97b031e-ed55-409d-95f2-aeb38c6fe366",
    "metadata": {},
    "outputs": [
@@ -1179,7 +1179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 33,
    "id": "f57bd746-dcbf-4433-8e24-ee213a8c34a1",
    "metadata": {},
    "outputs": [
@@ -1221,7 +1221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 34,
    "id": "e1770134-e7f3-4725-a679-e04c3be48cac",
    "metadata": {},
    "outputs": [
@@ -1229,7 +1229,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyTorch version: 2.1.0\n"
+      "PyTorch version: 2.2.2\n"
      ]
     }
    ],
@@ -1258,7 +1258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 35,
    "id": "74b41073-4c9f-46e2-a1bd-d38e4122b375",
    "metadata": {},
    "outputs": [],
@@ -1268,12 +1268,11 @@
     "\n",
     "class GPTDatasetV1(Dataset):\n",
     "    def __init__(self, txt, tokenizer, max_length, stride):\n",
-    "        self.tokenizer = tokenizer\n",
     "        self.input_ids = []\n",
     "        self.target_ids = []\n",
     "\n",
     "        # Tokenize the entire text\n",
-    "        token_ids = self.tokenizer.encode(txt, allowed_special={'<|endoftext|>'})\n",
+    "        token_ids = tokenizer.encode(txt, allowed_special={\"<|endoftext|>\"})\n",
     "\n",
     "        # Use a sliding window to chunk the book into overlapping sequences of max_length\n",
     "        for i in range(0, len(token_ids) - max_length, stride):\n",
@@ -1291,12 +1290,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 36,
    "id": "5eb30ebe-97b3-43c5-9ff1-a97d621b3c4e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def create_dataloader_v1(txt, batch_size=4, max_length=256, stride=128, shuffle=True, drop_last=True):\n",
+    "def create_dataloader_v1(txt, batch_size=4, max_length=256, stride=128, shuffle=True, drop_last=True, num_workers=0):\n",
     "\n",
     "    # Initialize the tokenizer\n",
     "    tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
@@ -1306,7 +1305,12 @@
     "\n",
     "    # Create dataloader\n",
     "    dataloader = DataLoader(\n",
-    "        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)\n",
+    "        dataset,\n",
+    "        batch_size=batch_size,\n",
+    "        shuffle=shuffle,\n",
+    "        drop_last=drop_last,\n",
+    "        num_workers=0\n",
+    "    )\n",
     "\n",
     "    return dataloader"
    ]
@@ -1321,7 +1325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 37,
    "id": "df31d96c-6bfd-4564-a956-6192242d7579",
    "metadata": {},
    "outputs": [],
@@ -1332,7 +1336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 38,
    "id": "9226d00c-ad9a-4949-a6e4-9afccfc7214f",
    "metadata": {},
    "outputs": [
@@ -1354,7 +1358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 39,
    "id": "10deb4bc-4de1-4d20-921e-4b1c7a0e1a6d",
    "metadata": {},
    "outputs": [
@@ -1398,7 +1402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 40,
    "id": "1916e7a6-f03d-4f09-91a6-d0bdbac5a58c",
    "metadata": {},
    "outputs": [
@@ -1473,7 +1477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 41,
    "id": "15a6304c-9474-4470-b85d-3991a49fa653",
    "metadata": {},
    "outputs": [],
@@ -1491,7 +1495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 42,
    "id": "93cb2cee-9aa6-4bb8-8977-c65661d16eda",
    "metadata": {},
    "outputs": [],
@@ -1513,7 +1517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 43,
    "id": "a686eb61-e737-4351-8f1c-222913d47468",
    "metadata": {},
    "outputs": [
@@ -1554,7 +1558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 44,
    "id": "e43600ba-f287-4746-8ddf-d0f71a9023ca",
    "metadata": {},
    "outputs": [
@@ -1581,7 +1585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 45,
    "id": "50280ead-0363-44c8-8c35-bb885d92c8b7",
    "metadata": {},
    "outputs": [
@@ -1874,7 +1878,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/ch02/01_main-chapter-code/dataloader.ipynb b/ch02/01_main-chapter-code/dataloader.ipynb
index 10978ef..01069d1 100644
--- a/ch02/01_main-chapter-code/dataloader.ipynb
+++ b/ch02/01_main-chapter-code/dataloader.ipynb
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "0ed4b7db-3b47-4fd3-a4a6-5f4ed5dd166e",
    "metadata": {},
    "outputs": [],
@@ -43,12 +43,11 @@
     "\n",
     "class GPTDatasetV1(Dataset):\n",
     "    def __init__(self, txt, tokenizer, max_length, stride):\n",
-    "        self.tokenizer = tokenizer\n",
     "        self.input_ids = []\n",
     "        self.target_ids = []\n",
     "\n",
     "        # Tokenize the entire text\n",
-    "        token_ids = self.tokenizer.encode(txt, allowed_special={'<|endoftext|>'})\n",
+    "        token_ids = tokenizer.encode(txt, allowed_special={\"<|endoftext|>\"})\n",
     "\n",
     "        # Use a sliding window to chunk the book into overlapping sequences of max_length\n",
     "        for i in range(0, len(token_ids) - max_length, stride):\n",
@@ -65,7 +64,7 @@
     "\n",
     "\n",
     "def create_dataloader_v1(txt, batch_size=4, max_length=256, \n",
-    "                         stride=128, shuffle=True, drop_last=True):\n",
+    "                         stride=128, shuffle=True, drop_last=True, num_workers=0):\n",
     "    # Initialize the tokenizer\n",
     "    tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
     "\n",
@@ -74,7 +73,7 @@
     "\n",
     "    # Create dataloader\n",
     "    dataloader = DataLoader(\n",
-    "        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)\n",
+    "        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=0)\n",
     "\n",
     "    return dataloader\n",
     "\n",
@@ -99,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "664397bc-6daa-4b88-90aa-e8fc1fbd5846",
    "metadata": {},
    "outputs": [],
@@ -117,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "d3664332-e6bb-447e-8b96-203aafde8b24",
    "metadata": {},
    "outputs": [
@@ -150,7 +149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/ch04/01_main-chapter-code/gpt.py b/ch04/01_main-chapter-code/gpt.py
index d7e9e8a..ff27673 100644
--- a/ch04/01_main-chapter-code/gpt.py
+++ b/ch04/01_main-chapter-code/gpt.py
@@ -13,13 +13,12 @@ from torch.utils.data import Dataset, DataLoader
 
 
 class GPTDatasetV1(Dataset):
-    def __init__(self, txt, tokenizer, max_length, stride):
-        self.tokenizer = tokenizer
+    def __init__(self, txt, tokenizer, max_length, stride, num_workers=0):
         self.input_ids = []
         self.target_ids = []
 
         # Tokenize the entire text
-        token_ids = self.tokenizer.encode(txt)
+        token_ids = tokenizer.encode(txt)
 
         # Use a sliding window to chunk the book into overlapping sequences of max_length
         for i in range(0, len(token_ids) - max_length, stride):
@@ -36,7 +35,7 @@ class GPTDatasetV1(Dataset):
 
 
 def create_dataloader_v1(txt, batch_size=4, max_length=256,
-                         stride=128, shuffle=True, drop_last=True):
+                         stride=128, shuffle=True, drop_last=True, num_workers=0):
     # Initialize the tokenizer
     tokenizer = tiktoken.get_encoding("gpt2")
 
diff --git a/ch04/01_main-chapter-code/previous_chapters.py b/ch04/01_main-chapter-code/previous_chapters.py
index 197cdb7..46d1e69 100644
--- a/ch04/01_main-chapter-code/previous_chapters.py
+++ b/ch04/01_main-chapter-code/previous_chapters.py
@@ -11,7 +11,6 @@ from torch.utils.data import Dataset, DataLoader
 
 class GPTDatasetV1(Dataset):
     def __init__(self, txt, tokenizer, max_length, stride):
-        self.tokenizer = tokenizer
         self.input_ids = []
         self.target_ids = []
 
@@ -33,7 +32,7 @@ class GPTDatasetV1(Dataset):
 
 
 def create_dataloader_v1(txt, batch_size=4, max_length=256,
-                         stride=128, shuffle=True, drop_last=True):
+                         stride=128, shuffle=True, drop_last=True, num_workers=0):
     # Initialize the tokenizer
     tokenizer = tiktoken.get_encoding("gpt2")
 
@@ -42,7 +41,7 @@ def create_dataloader_v1(txt, batch_size=4, max_length=256,
 
     # Create dataloader
     dataloader = DataLoader(
-        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)
+        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=0)
 
     return dataloader
 
diff --git a/ch05/01_main-chapter-code/.gitignore b/ch05/01_main-chapter-code/.gitignore
new file mode 100644
index 0000000..e69de29
diff --git a/ch05/01_main-chapter-code/ch05.ipynb b/ch05/01_main-chapter-code/ch05.ipynb
index d276286..97ec5bf 100644
--- a/ch05/01_main-chapter-code/ch05.ipynb
+++ b/ch05/01_main-chapter-code/ch05.ipynb
@@ -31,11 +31,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "matplotlib version: 3.8.4\n",
-      "numpy version: 1.26.4\n",
-      "tiktoken version: 0.6.0\n",
+      "matplotlib version: 3.7.2\n",
+      "numpy version: 1.25.2\n",
+      "tiktoken version: 0.5.1\n",
       "torch version: 2.2.2\n",
-      "tensorflow version: 2.16.1\n"
+      "tensorflow version: 2.15.0\n"
      ]
     }
    ],
@@ -931,7 +931,8 @@
     "    max_length=GPT_CONFIG_124M[\"context_length\"],\n",
     "    stride=GPT_CONFIG_124M[\"context_length\"],\n",
     "    drop_last=True,\n",
-    "    shuffle=True\n",
+    "    shuffle=True,\n",
+    "    num_workers=0\n",
     ")\n",
     "\n",
     "val_loader = create_dataloader_v1(\n",
@@ -940,7 +941,8 @@
     "    max_length=GPT_CONFIG_124M[\"context_length\"],\n",
     "    stride=GPT_CONFIG_124M[\"context_length\"],\n",
     "    drop_last=False,\n",
-    "    shuffle=False\n",
+    "    shuffle=False,\n",
+    "    num_workers=0\n",
     ")"
    ]
   },
@@ -1179,7 +1181,7 @@
    "outputs": [],
    "source": [
     "def train_model_simple(model, train_loader, val_loader, optimizer, device, num_epochs,\n",
-    "                       eval_freq, eval_iter, start_context):\n",
+    "                       eval_freq, eval_iter, start_context, tokenizer):\n",
     "    # Initialize lists to track losses and tokens seen\n",
     "    train_losses, val_losses, track_tokens_seen = [], [], []\n",
     "    tokens_seen, global_step = 0, -1\n",
@@ -1208,7 +1210,7 @@
     "\n",
     "        # Print a sample text after each epoch\n",
     "        generate_and_print_sample(\n",
-    "            model, train_loader.dataset.tokenizer, device, start_context\n",
+    "            model, tokenizer, device, start_context\n",
     "        )\n",
     "\n",
     "    return train_losses, val_losses, track_tokens_seen\n",
@@ -1302,7 +1304,7 @@
     "train_losses, val_losses, tokens_seen = train_model_simple(\n",
     "    model, train_loader, val_loader, optimizer, device,\n",
     "    num_epochs=num_epochs, eval_freq=5, eval_iter=5,\n",
-    "    start_context=\"Every effort moves you\",\n",
+    "    start_context=\"Every effort moves you\", tokenizer=tokenizer\n",
     ")"
    ]
   },
@@ -1321,7 +1323,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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S1PLly5W3t/c1L2159dVXVWJiopoxY8Y1L22pib8Dr7/+ulq3bp1KTk5We/bsUa+//rrSaDTqjz/+UErJ9q0Kl/f6Vkq28a2QoL6O6dOnq8DAQGVra6vatGmjtmzZYu6SLMKaNWsUcNXQp08fpZThEq2xY8cqX19fpdPpVMeOHVVSUpLJMs6ePauefvpp5eTkpFxcXFS/fv1Ubm6uSZvdu3er9u3bK51Op2rVqqU+/PDDq2r56aefVIMGDZStra1q2LChWrJkSZV97jvhWtsVULNnzza2uXDhgho0aJByd3dXDg4OqmfPniotLc1kOceOHVNdunRR9vb2ysvLS73yyiuquLjYpM2aNWtUs2bNlK2trapXr57JOsrUxN+B559/XgUFBSlbW1vl7e2tOnbsaAxppWT7VoUrg1q2ccVplFLKPPvyQgghhPgnco5aCCGEsGAS1EIIIYQFk6AWQgghLJgEtRBCCGHBJKiFEEIICyZBLYQQQlgwCeobKCoqYvz48RQVFZm7lBpJtm/Vku1b9WQbVy3ZvgZyHfUN5OTk4OrqSnZ2Ni4uLuYup8aR7Vu1ZPtWPdnGVUu2r4HsUQshhBAWTIJaCCGEsGA1/nnUJSUl7Nq1C19fX7Tain0vyc3NBeDkyZPk5ORURXl3Ndm+VUu2b9WTbVy1avL21ev1ZGRk0Lx5c6ytbxzFNf4c9fbt22nTpo25yxBCCCGusm3bNlq3bn3DNjV+j9rX1xcwbAx/f38zVyOEEEJAWloabdq0MWbUjdT4oC473O3v70/t2rXNXI0QQghR7mZOyZq1M9n69evp1q0bAQEBaDQaFi5caDJdKcW4cePw9/fH3t6emJgYDh06ZJ5ihRBCCDMwa1Dn5+fTtGlTZsyYcc3pH330EdOmTWPWrFls3boVR0dHYmNjKSwsvMOVCiGEEOZh1kPfXbp0oUuXLtecppRi6tSpvPXWW3Tv3h2Ab775Bl9fXxYuXMhTTz11J0sVQgghzMJiz1EnJyeTnp5OTEyMcZyrqytt27Zl8+bNEtRCiCpRWlpKcXGxucsQ1ZyNjQ1WVlaVsiyLDer09HSAq3rE+fr6GqddS1FRkcl9YcuuwxNCiBtRSpGenk5WVpa5SxE1hJubG35+fmg0mttajsUG9a2aOHEiEyZMqJqFl5bA6gkQfB+ExvxzeyFEtVEW0j4+Pjg4ONz2H1dx91JKUVBQQGZmJsBtXxpssUHt5+cHQEZGhsmHzMjIoFmzZtedb8yYMYwcOdL4/uTJk0RGRlZOUdu+gE3TYOf/YMBa8KhXOcsVQphVaWmpMaQ9PT3NXY6oAezt7QHIzMzEx8fntg6DW+y9voODg/Hz82P16tXGcTk5OWzdupWoqKjrzqfT6XBxcTEOzs7OlVbTL9pYjuoioDAb5sVBUV6lLVsIYT5l56QdHBzMXImoScp+nm63z4NZgzovL4+EhAQSEhIAQweyhIQEUlNT0Wg0jBgxgvfee49Fixaxd+9ennvuOQICAujRo8cdr/VU1gXe/P0gT2cPJt/GEzL3w6IhULPvwCrEXUUOd4vKVFk/T2YN6r/++ovmzZvTvHlzAEaOHEnz5s0ZN24cAK+99hpDhw5lwIABtG7dmry8PJYvX46dnd0drzXAzZ53ezQiAw/65g9Br7GGvxdA/Kd3vBYhhBB3D7MG9f33349S6qphzpw5gOHbyDvvvEN6ejqFhYWsWrWKBg0amK3e3q3q0LtVbbbrw5ik6WcYuXoCHF594xmFEKIaqVu3LlOnTr3p9mvXrkWj0VR5j/k5c+bg5uZWpeuwRBZ7jtpSvdO9EeF+znxecD+r7WNB6eGX5+FcsrlLE0LcZTQazQ2H8ePH39Jyt2/fzoABA266fbt27UhLS8PV1fWW1iduTIK6guxsrJj5TEucdDYMPP8vTjo2hMIs+PEZuJhv7vKEEHeRtLQ04zB16lRcXFxMxo0aNcrYVilFSUnJTS3X29u7Qh3rbG1tK+V6YXFtEtS3INjLkY8eb8JFbOh1diBFdl6QsQ9+k85lQog7x8/Pzzi4urqi0WiM7w8cOICzszPLli2jZcuW6HQ6Nm7cyJEjR+jevTu+vr44OTnRunVrVq1aZbLcKw99azQavvrqK3r27ImDgwOhoaEsWrTIOP3KQ99lh6hXrFhBREQETk5OdO7cmbS0NOM8JSUlDBs2DDc3Nzw9PRk9ejR9+vSpcGfhmTNnUr9+fWxtbQkLC+Pbb781TlNKMX78eAIDA9HpdAQEBDBs2DDj9P/+97+EhoZiZ2eHr68vjz/+eIXWfadIUN+ihxv707ddXTLwYEDhMJTWGv6eD5umm7s0IUQlUEpRcLHELIOqxC/8r7/+Oh9++CGJiYk0adKEvLw8Hn74YVavXs2uXbvo3Lkz3bp1IzU19YbLmTBhAr1792bPnj08/PDDxMXFce7cueu2LygoYPLkyXz77besX7+e1NRUkz38SZMm8f333zN79mzi4+PJycm56gmK/2TBggUMHz6cV155hX379vHvf/+bfv36sWbNGgB+/fVX/vOf//D5559z6NAhFi5cSOPGjQFDZ+Zhw4bxzjvvkJSUxPLly7n33nsrtP47xWJveFIdvPFwBAnHs1h3PIRZni8yMH8mrPsImsWBo9w0QYjq7EJxKZHjVphl3fvficXBtnL+PL/zzjs89NBDxvceHh40bdrU+P7dd99lwYIFLFq0iCFDhlx3OX379uXpp58G4IMPPmDatGls27aNzp07X7N9cXExs2bNon79+gAMGTKEd955xzh9+vTpjBkzhp49ewLw2WefsXTp0gp9tsmTJ9O3b18GDRoEGK4c2rJlC5MnT+aBBx4gNTUVPz8/YmJisLGxITAwkDZt2gCQmpqKo6MjjzzyCM7OzgQFBRmvQLI0skd9G2yttcyIa4Gbgw2TzrZnjW8feH65hLQQwmK0atXK5H1eXh6jRo0iIiICNzc3nJycSExM/Mc96iZNmhhfOzo64uLiYrxF5rU4ODgYQxoMt9Esa5+dnU1GRoYxNAGsrKxo2bJlhT5bYmIi0dHRJuOio6NJTEwE4IknnuDChQvUq1ePF198kQULFhjP0z/00EMEBQVRr149nn32Wb7//nsKCgoqtP47Rfaob1MtN3v+82Qz+s3eTr+UWD5Nd6e7n7mrEkLcLnsbK/a/E2u2dVcWR0dHk/ejRo1i5cqVTJ48mZCQEOzt7Xn88ce5ePHiDZdjY2Nj8l6j0aDX6yvUvjIP6d+MOnXqkJSUxKpVq1i5ciWDBg3i448/Zt26dTg7O7Nz507Wrl3LH3/8wbhx4xg/fjzbt2+3uEvAZI+6EjwQ5sOQB0IAGDN/L4czcyF1KywbLZ3LhKimNBoNDrbWZhmqsvd0fHw8ffv2pWfPnjRu3Bg/Pz+OHTtWZeu7FldXV3x9fdm+fbtxXGlpKTt37qzQciIiIoiPjzcZFx8fb/J8B3t7e7p168a0adNYu3YtmzdvZu/evQBYW1sTExPDRx99xJ49ezh27Bh//vnnbXyyqiF71JXk5YcasCPlPJuPnuX1b9bwc9G/0RQXgE8ktOxj7vKEEAKA0NBQ5s+fT7du3dBoNIwdO/aGe8ZVZejQoUycOJGQkBDCw8OZPn0658+fr9CXlFdffZXevXvTvHlzYmJi+P3335k/f76xF/ucOXMoLS2lbdu2ODg48N1332Fvb09QUBCLFy/m6NGj3Hvvvbi7u7N06VL0ej1hYWFV9ZFvmexRVxIrrYZPn26Gj7OOv85YscDjRVRkd2j0mLlLE0IIoylTpuDu7k67du3o1q0bsbGxtGjR4o7XMXr0aJ5++mmee+45oqKicHJyIjY2tkK3iO7RoweffvopkydPpmHDhnz++efMnj2b+++/HzA8D/rLL78kOjqaJk2asGrVKn7//Xc8PT1xc3Nj/vz5PPjgg0RERDBr1ix++OEHGjZsWEWf+NZp1J0+aXCHnThxgjp16nD8+HFq165d5evbevQs//pqK6V6PRN7NubptkFVvk4hxO0pLCwkOTmZ4OBgszxLQIBeryciIoLevXvz7rvvmrucSnGjn6uKZJPsUVeytvU8GdUpDNDw9u/72Xcy23Ceeuc3cNEyexQKIcSdlpKSwpdffsnBgwfZu3cvAwcOJDk5mX/961/mLs3iSFBXgX/fW4+O4T5cLNEz6PudFC0aCYuGGoaafQBDCCFuilarZc6cObRu3Zro6Gj27t3LqlWriIiIMHdpFkc6k1UBrVbDJ72b0nXaRlLPFTAtvTGjtNZo9v0CAc2h3fVvKiCEEHeDOnXqXNVjW1yb7FFXETcHW2Y+0wJbKy0zkn3ZHDLSMGHlWDi61qy1CSGEqD4kqKtQk9pujH3EcBjnuX3NOFP/McNjMX/uB+dTzFydEEKI6kCCuoo9c08Q3ZoGUKKHx1Ifp8S3KVw4Bz/GSecyIYQQ/0iCuoppNBom9mpMPW9HUnIVr2hfRTl4Qfpe+H24dC4TQghxQxLUd4CTzpqZcS2xs9HyW7KWX+u9Bxor2PsTbJlp7vKEEEJYMAnqOyTMz5kPehqeg/rqDheOtHjDMOGPtyB5vRkrE0IIYckkqO+gXi1q83SbOigFT+xqQkHE46BK4ee+kHXjR8wJIURVuf/++xkxYoTxfd26dZk6deoN59FoNCxcuPC2111Zy7mR8ePH06xZsypdR1WSoL7D3u7WkEh/F84VFNP/7DMov6ZQcNbQE1zOVwshKqBbt2507tz5mtM2bNiARqNhz549FV7u9u3bGTBgwO2WZ+J6YZmWlkaXLl0qdV01jQT1HWZnY8XMZ1rgrLNmc2oBn/m8bXjCVqd3oQofbSeEqHn69+/PypUrOXHixFXTZs+eTatWrWjSpEmFl+vt7Y2Dg0NllPiP/Pz80Ol0d2Rd1ZUEtRkEeTry8ROGX55PthWy4t5fIaidmasSQlQ3jzzyCN7e3syZM8dkfF5eHj///DP9+/fn7NmzPP3009SqVQsHBwcaN27MDz/8cMPlXnno+9ChQ9x7773Y2dkRGRnJypUrr5pn9OjRNGjQAAcHB+rVq8fYsWMpLi4GDI+bnDBhArt370aj0aDRaIw1X3noe+/evTz44IPY29vj6enJgAEDyMvLM07v27cvPXr0YPLkyfj7++Pp6cngwYON67oZer2ed955h9q1a6PT6WjWrBnLly83Tr948SJDhgzB398fOzs7goKCmDhxIgBKKcaPH09gYCA6nY6AgACGDRt20+u+FXILUTPp3MifF9oH89XGZEb9spcIfzcCPR3g1C5I+AE6TwStlbnLFEJczK/4PFY6sLr057W0BEqLQKMFG/t/Xq6t402vxtramueee445c+bw5ptvGp/l/PPPP1NaWsrTTz9NXl4eLVu2ZPTo0bi4uLBkyRKeffZZ6tevT5s2bf5xHXq9nl69euHr68vWrVvJzs42OZ9dxtnZmTlz5hAQEMDevXt58cUXcXZ25rXXXuPJJ59k3759LF++3PisaFdX16uWkZ+fT2xsLFFRUWzfvp3MzExeeOEFhgwZYvJlZM2aNfj7+7NmzRoOHz7Mk08+SbNmzXjxxRdvart9+umnfPLJJ3z++ec0b96cr7/+mkcffZS///6b0NBQpk2bxqJFi/jpp58IDAzk+PHjHD9+HIBff/2V//znP8ybN4+GDRuSnp7O7t27b2q9t8qig7q0tJTx48fz3XffkZ6eTkBAAH379uWtt96q0MPFLdXoLuHsOp7FjpTzDPx+B78+3xi77x4znLN28Yf2L5u7RCHEBwEVn+eJOdCwp+H1gd8NHUaD2kO/JeVtpjY2/K5faXx2hVb1/PPP8/HHH7Nu3Trjc5hnz57NY489hqurK66urowaNcrYfujQoaxYsYKffvrppoJ61apVHDhwgBUrVhAQYNgWH3zwwVXnld966y3j67p16zJq1CjmzZvHa6+9hr29PU5OTlhbW+Pn53fddc2dO5fCwkK++eYbHB0NX1g+++wzunXrxqRJk/D19QXA3d2dzz77DCsrK8LDw+natSurV6++6aCePHkyo0eP5qmnngJg0qRJrFmzhqlTpzJjxgxSU1MJDQ2lffv2aDQagoLKH1ecmpqKn58fMTEx2NjYEBgYeFPb8XZY9KHvSZMmMXPmTD777DMSExOZNGkSH330EdOnTzd3aZXCxkrLZ/9qjoejLX+fymHc8hRUl4+hbgdo/YK5yxNCVAPh4eG0a9eOr7/+GoDDhw+zYcMG+vfvDxh2eN59910aN26Mh4cHTk5OrFixgtTUm7vSJDExkTp16hhDGiAqKuqqdj/++CPR0dH4+fnh5OTEW2+9ddPruHxdTZs2NYY0QHR0NHq9nqSkJOO4hg0bYmVVfsTR39+fzMzMm1pHTk4Op06dIjo62mR8dHQ0iYmJgOHwekJCAmFhYQwbNow//vjD2O6JJ57gwoUL1KtXjxdffJEFCxZQUlJSoc9ZURa9R71p0ya6d+9O165dAcO3tB9++IFt27aZubLK4+9qz9Qnm9F39jZ++usEgR5NGPLcItBe9h1KKeloJoS5vHGq4vNYXdY5KrybYRmaK/aLRuy9vbou079/f4YOHcqMGTOYPXs29evX57777gPg448/5tNPP2Xq1Kk0btwYR0dHRowYwcWLFytt/Zs3byYuLo4JEyYQGxuLq6sr8+bN45NPPqm0dVzOxsbG5L1Go0Gv11fa8lu0aEFycjLLli1j1apV9O7dm5iYGH755Rfq1KlDUlISq1atYuXKlQwaNMh4ROPKuiqLRe9Rt2vXjtWrV3Pw4EEAdu/ezcaNG2/Ylb+oqIicnBzjkJube6fKvWX3NvBm/KMNAZj8x0HmJ1z2h2HDJ7D0Vbl0SwhzsXWs+GB12T6QlbVh3OXnp2+03FvQu3dvtFotc+fO5ZtvvuH55583nh6Mj4+ne/fuPPPMMzRt2pR69eoZ/6bejIiICI4fP05aWppx3JYtW0zabNq0iaCgIN58801atWpFaGgoKSmmDx6ytbWltLT0H9e1e/du8vPLz9/Hx8ej1WoJCwu76ZpvxMXFhYCAgKsesRkfH09kZKRJuyeffJIvv/ySH3/8kV9//ZVz584BYG9vT7du3Zg2bRpr165l8+bN7N1beV+8rmTRe9Svv/46OTk5hIeHY2VlRWlpKe+//z5xcXHXnWfixIlMmDDhDlZZOZ6LqsvJ8xf4fP1RXvtlD74udkQ7Z8DqdwFl6FjW+UPZsxZCXMXJyYknn3ySMWPGkJOTQ9++fY3TQkND+eWXX9i0aRPu7u5MmTKFjIwMk1C6kZiYGBo0aECfPn34+OOPycnJ4c033zRpExoaSmpqKvPmzaN169YsWbKEBQsWmLSpW7cuycnJJCQkULt2bZydna+6LCsuLo63336bPn36MH78eE6fPs3QoUN59tlnjeenK8Orr77K22+/Tf369WnWrBmzZ88mISGB77//HoApU6bg7+9P8+bN0Wq1/Pzzz/j5+eHm5sacOXMoLS2lbdu2ODg48N1332Fvb29yHruyWfQe9U8//cT333/P3Llz2blzJ//73/+YPHky//vf/647z5gxY8jOzjYO+/fvv4MV357RncN5pIk/JXrFS9/u4ICqA49eOh+/dRaseFP2rIUQ19S/f3/Onz9PbGysyfnkt956ixYtWhAbG8v999+Pn58fPXr0uOnlarVaFixYwIULF2jTpg0vvPAC77//vkmbRx99lJdffpkhQ4bQrFkzNm3axNixY03aPPbYY3Tu3JkHHngAb2/va14i5uDgwIoVKzh37hytW7fm8ccfp2PHjnz22WcV2xj/YNiwYYwcOZJXXnmFxo0bs3z5chYtWkRoaChg6MH+0Ucf0apVK1q3bs2xY8dYunQpWq0WNzc3vvzyS6Kjo2nSpAmrVq3i999/x9PTs1JrvJxGKcv9y1+nTh1ef/11Bg8ebBz33nvv8d1333HgwIGbWsaJEyeoU6cOx48fp3bt2lVVaqUpLC7lua+3sS35HP6udswf1A7/wz8anrQF0G4oPCQ3RxGiMhUWFpKcnExwcDB2dnbmLkfUEDf6uapINln0HnVBQQFarWmJVlZWldppwNLY2VjxxbMtqe/tSFp2If1mbye3YRx0nWJosGk6rJ4ge9ZCCHGXsOig7tatG++//z5Llizh2LFjLFiwgClTptCzZ09zl1al3BxsmdOvDd7OOg6k5zLwu51cbN4PHp5saLDxP/DnexLWQghxF7DooJ4+fTqPP/44gwYNIiIiglGjRvHvf/+bd99919ylVbk6Hg7M7tsaB1srNh4+w+vz96BavwCdJxkabJgMayeat0ghhBBVzqJ7fTs7OzN16tR/fNxaTdWolisz4lrwwv/+Yv7Ok9R2s2dkp5cMj8Zc8QasmwQaK7h/tLlLFUIIUUUseo9awANhPrzfoxEA0/48zLxtqRA12NChDGDtB7B+shkrFEIIUZUkqKuBp9oEMvTBEADeXLiPNUmZED0MYsYbGvz5LqRV/JmzQghTNbmjqrjzKuvnyaIPfYtyIx9qwMmsC8zfeZLB3+/kp39H0aj9y6D04OAF/hV/5qwQwsDW1hatVsupU6fw9vbG1ta2Rjz4R5iHUoqLFy9y+vRptFottra2t7U8CepqQqPR8GGvJmTmFLHx8Bn6zdnO/IHtqNPhFdOGJUVgLQ9hF6IitFotwcHBpKWlcerULdzbW4hrcHBwIDAw8KrLjCtKgroasbXW8t9nWtB71mYOpOfSb852fn2pHa4Ol24En38GvukOzZ+Fe14yb7FCVDO2trYEBgZSUlLyj/ekFuKfWFlZYW1tXSlHZiSoqxkXOxtm92tNzxmbOJyZx4vf/sW3/dugs7aCfb9Cxj7DddbNnga7qx/MLoS4Po1Gg42NTZU9BUmIWyGdyaohf1d75jzfGmedNduSz/HKT7vR6xW0GQAd34a+SySkhRCihpCgrqbC/VyY9WxLbKw0LN6TxqTlBwz3/+4wErxCyhvmZpivSCGEELdNgroaiw7xYtJjht7en68/yjebj5k2OLQKPm0Ku76788UJIYSoFBLU1VyvFrV55aEGAIxf9Dcr91+2B310DZRcgN+GwDc9YMf/oOCceQoVQghxSySoa4AhD4bwVOs66BUM/WEnu1LPGyZ0eg/uGQQoQ2j/Pgwmh8J3j0PCXCjMNmvdQggh/pkEdQ2g0Wh4r0cj7g/zprBYzwv/+4uUs/mGc9adJ8LQnfDgWPBtDPoSOLwSFg6Ej0Ng7lOw5ycoyjX3xxBCCHENGqVq9rMSK/Jw7uouv6iEJ7/YzL6TOQR7OfLrwHZ4OF5xR5zTB+HvBfD3fDh9oHy8lQ5CH4JH/gNOPne2cCGEuMtUJJtkj7oGcdRZ83Xf1tRysyf5TD4v/G87hcVX3LjBu4HhaVuDt8LAzXDva+AZAqVFkBIP9u7lbTMTofjCnf0QQgghTEhQ1zA+znb87/nWuNrbsDM1i+HzdlGqv85BE99IePBNGPIX/HsDdJsGVpdu9KAUfN/bcHj8xI479wGEEEKYkKCugUJ8nPnyuVbYWmlZ8XcG7y7ezw3PcGg0hod6RD5aPi43DVCGwPaJKB9/YCkcWgmlxVVWvxBCiHJyC9Eaqk2wB5/0bsrQH3YxZ9MxUs7mM+bhCBr4Ot/cAlwCYPgeOJ8Mtg6GcUrBqvFwJslwiDyiG9S5B7TWoLUyDJor/9WCX+Py894F5+BcMti5gFdo+frOJQPq0nzWhj17By+4zZvZCyFEdSdBXYN1axrA2bwi3luSyJqk06w7eJonWtZhZKcG+LrY/fMCtFrwrF/+vvQiBN8LF85B/mnY+Y1h+CdPzIGGPQ2vj66FX/pB3Q7Qd3F5my8fNCz3cjoX8G96aWgGAc3Ao76EtxDiriJBXcP1jQ7m3gbefLQ8ieV/p/PjX8dZtPsUL3YIZsB99XHSVeBHwFoHXSdDl0lwbCPsXwjnU0CVgr7U8Gxsfell70tBrwc7t8uWYQeugVf3LLd1MnwR0JcY5tWXQFEOHNtgGC5v59fEENrNnzWcZxdCiBpMLs+6i/x17BwfLE1kZ2oWAF5OtgyPacBTretgY2Vhe6mlxXA6CdIS4FSC4d/0fYY7rZV5Zj6EdDS8Tt4ABxZDSIzhMjMhhLhZSkFJIVzMh4t5l/4te11Q/trBExr2qJRVViSbZI/6LtKqrge/DmzH8n3pTFp+gGNnCxi7cB+z45N5vXM4D0X6VsqzUyuFlQ34NTIMzZ8xjCstgTMHy8M7oHl5+8OrYOsswy9bWVAXF8LKcYZD5wHNwCsMrORHXogaQynDzZoKzhr6vxScNQyFWeBSq7yDrFKGU25FedDrC3DwMIxf/Q5s+9IQwkr/z+sLjKq0oK4I+at1l9FoNHRp7E9MpC9zt6by6epDHD2dz4Bvd9CmrgdjHg6neaD7Py/IHKysDYe6fSOh2b9Mp9V/oPwcepnM/bDt8/L31nbg28jQi93ezXAOXOd8xXDpvLiVPI9YiDtOrzdccVJw1vC7WtYf5e8FhtNtZUF8eSiXXrz2skIeKg9qjQYO/gHF+YZbJ5cFtb7UcIrtcjYOYOt46V8nw+uy4fIrYO4gOfR9l8spLObzdUf4akMyRSWGb5Rdm/jzWmwYQZ6OZq7uNp09An99fenQ+W64eJO3SX09tfx53r+PgL2/wANvQNQgw7jzxwx76mXBrnM2/EKX/WvrADb2YONo+Nf20r9Ovoae8ELUBHq94QiWjb0hCAHOHYXcdCguMNwsqfiC4bBx8QXTccWXDidfOAcBLQz3cwAouQjveRtev5ZcHqiLR8Jf/3f9WmwcDIelHTwM/9q5Gb5wtx9R3mbnN4arUCK6lf9+52YY9qbLgtjG4Y79jsqhb3HTXOxseDU2nGfuCeKTPw7y684TLNmTxh9/p/PMPUEMfTD06tuQVhee9SH2fcNrvd7wRyQtwfBvUY7hkNlVQ44hbMsUZhsCXnPZOfzcdNj/W8XrGbEP3OoYXv/5Huz8Fu4ZWP7HJDcdFr98KeQdygPextEQ/raO5V8IjF8OnMClNlhX0/+jmqakyHRv7/I9wAvnLrv/gILwroY+FQBZqbBukiFgyn5mAdZOMvy8lt3T4B//BcK6lB9xyj8Li4YYLnl88lvT5Z786+p5r7Xc0mJDsIbGlgdqYTZ8GGh4/VamoaMpwNoPYc+PFdtml+8rWtuCvYfhiFZRbnlQh3a6FMSepoFcNpRdQnojLZ67epyzL+BbsXrNwOKD+uTJk4wePZply5ZRUFBASEgIs2fPplWrVuYurUbxd7Vn8hNN6d8+mInLDrD+4Glmxx/jlx0nGHR/CP2i62JnU433BrVa8AoxDBXR9RN48C3TW6u6BcLDk68O+8Icw6G14guGDijFBeV7ERfzDUFbpuAs5KUb9kiM485B0tKKf7aX4g3n8gE2z4AtMw1/qB94wzCuKA+WvWYa7pcfAbC2M/SyV6WX9bovNXTUK/tDmbYbUrcYbjdb1oGv5CJs+OSy+S6bt+y9lY3hPvLWlw0Rj5Zf9pd13PDlydkfal/2O336oGHPxtqufD4rnWF5d6ofRWkJXDhvWLedi2Hc+WOw92ewdYZ7Xipv+3+dIGP/zR+1AXCtXR7UBWcNz413DjAN6kN/GAK1IlzrlL8uuWD4mbK64ovcqZ2GZVeE52X3PbC5LBiLC8qD2iXA8DNy+RElm7IjTJe9LvsSau8BHsGm63nt6NX/x2GdDcNdyqKD+vz580RHR/PAAw+wbNkyvL29OXToEO7uFnoOtQaI8Hfhm+fbsOHQaT5YeoDEtBwmLT/At5uPMSo2jB7NaqHVWkiHszvBwaM8rMq4BECbFyu2nCvPMN03Glr2BUfv8nHOftDt02uHfHGBIXAv5hm+FJT9W5RnCN4yeRmQfdwwvkxhFiR8X7F6AV5YXf7Zj66DlWOhyVPlQa1KYd2HFV+ud3h5UB/bCAtfgvoPwrMLytt8+eB1Qk9THtoazaVBaxjfZRI0fry83oUDDTfb+ddle3izu0LuqfJ5NFrTZWi0hm1d1iEJDF/Kyv6/s08YjoZ4hpgG9cWC8no1Vlfs8V16be9+KTAv1R3Yrnx+5wDDE+50V9yQqO2/Ibf7peDSXP0vXDEOw2cuY+dm+Jm6/IgQQNuXDIeAb7isS/9a2RgC1qVW+fxWNjDq8KXTPJeFdsx4w3A7LKVDqwWx6KCeNGkSderUYfbs2cZxwcHBN5hDVJYOod4sHurFwl0nmfxHEqeyCxn5026+2pDMGw9H0D7Uy9wlVi9X/vFx9jMMl3PwMIT37bhnEER0N/1yYesIHcddFvR5hlApC/rSItDaXLq7nHX5neYuPwLg1cBw05paLcvHaW2gVf/L5tFe9traEFj6EsNRg5Iiw3pKikz3+Bw8oE5b8L6ik07Zl4/Sois6C126jObyIxFlSorKXxdfgJyThn4Bl8tKMXyRqYiLl33pcQs0XIXgGmja5rGvLt1NzwN0rhW/KY+zL9w76urxTXpXbDlX0jld+2eq/gO3t1wAJ+9/biMqhUV3JouMjCQ2NpYTJ06wbt06atWqxaBBg3jxxZvfm5HOZLevsLiUr+OTmbnmCLlFJQDc18Cb17uEE+HvYubqRI2n15eHvDHwL2I4j6o3HK1QenDxLz9FUZhtOLdr4wDeYeXLOrnzUqCr8vmufG1tV743bOcml/SJKlGRbLLooLazM9zmcuTIkTzxxBNs376d4cOHM2vWLPr06XPNeYqKiigqKv9mffLkSSIjIyWoK8G5/ItMW32I77akUKJXaDTQMdyXZnVcifB3IcLfBX9XO8u5FlsIISxUjQlqW1tbWrVqxaZNm4zjhg0bxvbt29m8efM15xk/fjwTJky4arwEdeU5diafj1cksWRv2lXT3BxsiPBzuRTczkT4uxDq64TOuhp3RBNCiEpWYy7P8vf3JzLS9F7OERER/Prrr9edZ8yYMYwcOdL4vmyPWlSeul6OzIhrwcCT2Ww6cobEtFwS03I4nJlHVkExm4+eZfPRs8b21loNIT5OJuEd4e+Cl5POjJ9CCCGqB4sO6ujoaJKSkkzGHTx4kKCgoOvOo9Pp0OnKAyAnJ+e6bcXtaVTLlUa1XI3vi0pKOZSRR2JajjG896flkH2hmAPpuRxIz2XBrvL5vZ11RPqX731H+rsQ7OWItaXdd1wIIczoloL6+PHjaDQa4+76tm3bmDt3LpGRkQwYMKDSinv55Zdp164dH3zwAb1792bbtm188cUXfPHFF5W2DlF5dNZWV4W3Uoq07MJL4W0I8P1pORw7m8/p3CLW5Roev1m+DC1hfobQ7tTQl/sa+GB1N10OJoQQV7ilc9QdOnRgwIABPPvss6SnpxMWFkbDhg05dOgQQ4cOZdy4cZVW4OLFixkzZgyHDh0iODiYkSNHSq/vGiC/qISkjEt73acMIX4gPZeCi6Um7Wq52fOvtoE82bqOHCoXQtQYVd6ZzN3dnS1bthAWFsa0adP48ccfiY+P548//uCll17i6NGjt1x8ZZOgrj70ekXquQIS03LYfuw883edIKvAcMtFGysNXRr582xUEK2C3KVnuRCiWqvyzmTFxcXG88CrVq3i0UcNTygJDw8nLe3qnsBC3AytVkNdL0fqejnSpbE/r3UOY/GeNL7bkkLC8SwW7T7Fot2nCPN15pmoIHo2r4WTzqK7WQghxG27pV47DRs2ZNasWWzYsIGVK1fSubPhHqynTp3C09OzUgsUdy87Gyseb1mbhYOj+X1Ie55sVQc7Gy1JGbmMXbiPtu+v4q2FezmQLh0GhRA11y0d+l67di09e/YkJyeHPn368PXXXwPwxhtvcODAAebPn1/phd4qOfRds2RfKObXHSf4bmsKR0/nG8e3ruvOM/cE0bmRn1yzLYSweHfkhielpaXk5OSYPCDj2LFjODg44OPjcyuLrBIS1DWTUorNR87y3dYUVvydQane8GPs6WjLk63r8HSbQOp43MSj74QQwgyq/Bz1hQsXUEoZQzolJYUFCxYQERFBbGzsrSxSiArRaDS0C/GiXYgXGTmFzNt2nLnbUsjIKeK/a48wc90RHgzz4Zl7gri3gbdc4iWEqLZuaY+6U6dO9OrVi5deeomsrCzCw8OxsbHhzJkzTJkyhYEDB1ZFrbdE9qjvHiWlelYlZvLdlhQ2Hj5jHF/b3Z64tkH0blUbT7nESwhhASqSTbfUmWznzp106NABgF9++QVfX19SUlL45ptvmDZt2q0sUojbZm2lpXMjP757oS1/vnIf/dsH42JnzYnzF5i0/ABRE/9kxLxd7Eg5b+5ShRDipt1SUBcUFODsbHjA+R9//EGvXr3QarXcc889pKSkVGqBQtyKet5OjH0kkq1vxPDR401oUtuVi6V6Fiac4rGZmxg8dycZOdd4prEQQliYWwrqkJAQFi5cyPHjx1mxYgWdOnUCIDMzExcXeT6xsBz2tlb0blWHRUPas2hINI+3rI1WA0v2pNHxk3XMjk82dkQTQghLdEtBPW7cOEaNGkXdunVp06YNUVFRgGHvunnz5pVaoBCVpUltNyY/0ZRFQ9rTrI4beUUlTPh9P91nbGT38SxzlyeEENd0y5dnpaenk5aWRtOmTdFqDXm/bds2XFxcCA8Pr9Qib4d0JhPXotcrftieyqRlB8gpLEGjgWfaBjEqNgxXextzlyeEqOHuyHXUl68MsNgQlKAWN3I6t4iJSxOZv+skAF5OOsY+EsGjTQPkfuJCiCpT5b2+9Xo977zzDq6urgQFBREUFISbmxvvvvsuer3+looWwhy8nXVMebIZc19oSz1vR87kFTF8XgLP/N9Wjp7OM3d5Qghxa0H95ptv8tlnn/Hhhx+ya9cudu3axQcffMD06dMZO3ZsZdcoRJVrF+LFsuEdGNWpATprLfGHz9J56gamrDxIYXHpPy9ACCGqyC0d+g4ICGDWrFnGp2aV+e233xg0aBAnT56stAJvlxz6FhWVcjafcb/9zbqDpwEI8nTgne6NuK+Bt5krE0LUFFV+6PvcuXPX7DAWHh7OuXPnbmWRQliMIE9H5vRrzX/jWuDroiPlbAF9vt4m114LIcziloK6adOmfPbZZ1eN/+yzz2jSpMltFyWEuWk0Gh5u7M+qkffxfHSwXHsthDCbWzr0vW7dOrp27UpgYKDxGurNmzdz/Phxli5dary9qCWQQ9+iMuw7mc2bC/cZr7duVMuF93s0pmkdN7PWJYSonqr80Pd9993HwYMH6dmzJ1lZWWRlZdGrVy/+/vtvvv3221sqWghL1qiWK/MHtuO9Ho1wtrNm38kcevw3nrEL95F9odjc5QkharDbvo76crt376ZFixaUllpOL1nZoxaV7XRuER8sTWSBXHsthLhFVb5HLcTdzNtZx3/Krr32Mr32es+JLPRy/loIUYmszV2AENVVuxAvlo3owBfrjjJ9zWHiD5/l0c/i8XKypX2IF/c28KZ9iBc+LnbmLlUIUY1JUAtxG3TWVgztGMqjzQL4aHkSfx7I5EzeRRYmnGJhwikAwv2cubeBNx1CvWhd1wM7GyszVy2EqE4qFNS9evW64fSsrKzbqUWIaivI05EZcS0oKillZ0oWGw6dZsOhM+w7lc2B9FwOpOfyxfqj6Ky1tAn24N5Qbzo08CLM11nOawshbqhCQe3q6vqP05977rnbKkiI6kxnbUVUfU+i6nvyWmc4m1dE/JGzbDhoCO70nEI2HDrDhkNnYKnhfHeHUC/uDfUmOsQLb2eduT+CEMLCVGqv76r24YcfMmbMGIYPH87UqVNvah7p9S0shVKKw5l5rD90hg2HTrPl6FkKi00fYhPp70KHBobgblXXHZ21HCYXoiaqSDZVm3PU27dv5/PPP5c7n4lqS6PREOrrTKivM/3bB1NYXMrOlPPG4P77VA770wzD5+uOYmej5Z56nnQINZzfDvVxksPkQtyFqkVQ5+XlERcXx5dffsl7771n7nKEqBR2Nla0C/GiXYgXr3cJ50xeEfGHz7Du0mHy07lFrE06zdokw8NBfJx1RId4XRo88Xe1N/MnEELcCdUiqAcPHkzXrl2JiYn5x6AuKiqiqKjI+D43N7eqyxOiUng56ejerBbdm9VCKUVSRi4bDp5h/aHTbEs+R2ZuEQt2nTTeaKWetyPtLwX3PfU8cbW3MfMnEEJUBYsP6nnz5rFz5062b99+U+0nTpzIhAkTqrgqIaqWRqMh3M+FcD8XXry3nvEw+cbDZ4g/cpa9J7I4ejqfo6fz+WZzCloNNK7tRnR9T9qHeNEiyF0uAxOihrDozmTHjx+nVatWrFy50nhu+v7776dZs2bX7Ux25R71yZMniYyMlM5kokbJLihm89GzbDpyho2Hz3D0dL7JdJ21ltZ1PYgO8aJ9iBeRAS5YaeX8thCWoiKdySw6qBcuXEjPnj2xsirfMygtLUWj0aDVaikqKjKZdi3S61vcDdKyLxB/+Czxh88Qf/gMmblFJtNd7W1oV9+TdpeCu66ng3RME8KMakxQ5+bmkpKSYjKuX79+hIeHM3r0aBo1avSPy5CgFnebssvANh4+Q/zhs2w5epa8ohKTNrXc7GlX35P2oYbg9nSS67eFuJNqzOVZzs7OV4Wxo6Mjnp6eNxXSQtyNLr8MrF90MCWlenafyGbTYcNh8p2p5zmZdYGfd5zg5x0n0FlrGdMlnOei6qKVw+NCWByLDmohxO2zttLSMsidlkHuDO0YSsHFErYfO2+4FCzpNEkZuYz/fT+rEjP5+IkmctmXEBbGog99VwY59C3E9Sml+HZLCh8sTaSwWI+LnTXv9mhE92a1zF2aEDWaPI9aCHFTNBoNz0XVZcmwDjSt7UpOYQnD5yUwZO5Osgoumrs8IQQS1EIIoL63E78MbMeImFCstBoW70kjdup61h08be7ShLjrSVALIQCwsdIyIqYB8we2o563Ixk5RfT5ehvjftvHhYul5i5PiLuWBLUQwkTTOm4sGdqBPlFBAHyzOYWu0zaQcDzLvIUJcZeSoBZCXMXe1ooJ3Rvxbf82+LnYcfRMPo/N3MR/Vh6kuFT/zwsQQlQaCWohxHV1CPVmxYh7ebRpAKV6xaerD/HYzE0czswzd2lC3DUkqIUQN+TqYMO0p5sz7enmuNhZs+dENl2nbWBOfDJ6fY2+ulMIiyBBLYS4KY82DeCPl++jQ6gXRSV6xv++n+e+3kZa9gVzlyZEjSZBLYS4aX6udnzzfBve6d4QOxstGw+fIfY/6/kt4aS5SxOixpKgFkJUiNwkRYg7S4JaCHFL5CYpQtwZEtRCiFsmN0kRoupJUAshbtv1bpKyan8GF0vkumshboc85lIIUSnKbpISE+nLqz/v4eiZfF745i9c7Kx5KNKPrk38aB/ija217B8IURES1EKISlV2k5RPVx9i8Z5TZOYW8evOE/y68wTOdtY8FOnLw4386dDAC521lbnLFcLiyfOohRBVplSv2JFynqV701i6N43M3CLjNGedNTGRvjzc2J8OoV7Y2Uhoi7tHRbJJgloIcUfo9YodqedZsieNZfvSyMgpD20nnTUxET483Nifext4S2iLGk+C+jIS1EJYHr1esTP1PEv2prFsbzrpOYXGaU46azpeCu37JLRFDSVBfRkJaiEsm16v2HX8PEv2pLNsXxpp2eWh7WhrRccIw+Hx+8MktEXNIUF9GQlqIaoPQ2hnsXRvGsv2pnHqitB+MMKXro39uD/MR0JbVGsS1JeRoBaietLrFQknsli6J41l+9I5mVX+8A97Gyta1XUnqr4nUfU8aVzLFWsruexLVB8VySa5PEsIYZG0Wg0tAt1pEejOm10j2H0im6V701iyJ42TWRfYcOgMGw6dAQzntVsbg9uLyAAXrLQaM38CISqHBLUQwuJpNBqa1XGjWR03xnQJJykjl81HzrL5yFm2Jp8j+0Ixa5JOsybJcJ9xZztr2gZ7cE89T6LqexLh54JWgltUUxLUQohqRaPREO7nQrifC/2igynVKxLTcthy1BDc25LPkVtYwqrETFYlZgLg5mBD22APoup5ElXfiwa+Tmg0EtyierDooJ44cSLz58/nwIED2Nvb065dOyZNmkRYWJi5SxNCWAgrrYZGtVxpVMuVFzrUo6RUz9+ncth8Kbi3HztHVkExK/7OYMXfGQB4OtpyTz1P7rl0jru+t6MEt7BYFt2ZrHPnzjz11FO0bt2akpIS3njjDfbt28f+/ftxdHS8qWVIZzIh7m7FpXr2nsxm85GzbDlqCO7CYtMHhXg764iq58k99TxpXdedYC9H6ZwmqlSN7fV9+vRpfHx8WLduHffee+9NzSNBLYS43MUSPbtPZBnPce9IPX/VE75srbTU93EizNeJMD8Xwv2caeDnTICrnex5i0pRY3t9Z2dnA+Dh4WHmSoQQ1ZWttZbWdT1oXdeDYR1DKSwuZVdqFpuPnmXLkbPsO5VNwcVSEtNySEzLAU4Z53W2sybM15kwv0uDrzPhfi64OtiY7wOJGq/a7FHr9XoeffRRsrKy2Lhx43XbFRUVUVRUfg/hkydPEhkZKXvUQoibotcrTmZd4EB6LgczcjmQnktSeg5HT+dTor/2n0tfF51xz7ssyEN8nOSmLOK6auQe9eDBg9m3b98NQxoMHdAmTJhwh6oSQtQ0Wq2GOh4O1PFw4KFIX+P4iyV6jp7JIyk91zgcSM/lZNYFMnKKyMg5zfqDp8uXo4G6Xo7G4A73cyY6xAtnO9n7FhVTLfaohwwZwm+//cb69esJDg6+YVvZoxZC3Em5hcUczCgL8BySMgwhfr6g+Kq2TjprnmhVm37tggn0dDBDtcJS1Jg9aqUUQ4cOZcGCBaxdu/YfQxpAp9Oh0+mM73NycqqyRCHEXc7ZzoaWQe60DHI3jlNKcTq3yBjaB9Jz2ZFynuQz+cyOP8b/Nh3joUhf+revR+u67tJBTdyQRQf14MGDmTt3Lr/99hvOzs6kp6cD4Orqir29vZmrE0KIa9NoNPi42OHjYkeHUG/AEN7rDp7m6/hjrD942nhdd+NarvRvH8zDjf2xtZZLwsTVLPrQ9/W+Zc6ePZu+ffve1DLk8iwhhKU5mJHL7Phk5u88SdGlS8N8XXQ8F1WXf7UJxN3R1swViqpWY6+jvhUS1EIIS3U2r4i5W1P5ZksKp3MNfWvsbLT0alGb56ODCfFxMnOFoqpIUF9GgloIYemKSkpZvDuN/9uYzP608n4194d50799MO1DvOQ8dg1TYzqTCSHE3UBnbcVjLWvTq0Uttiaf4/82JrMqMYO1SadZm3SaMF9nnm9fl+7Nasm12Xch2aMWQggLdOxMPnM2HeOnv45TcLEUMDxMJO6eIJ69JwhvZ90/LEFYMjn0fRkJaiFEdZZ9oZgft6fyv00pnMy6ABjuRd6taQD92wcTGeBi5grFrZCgvowEtRCiJigp1bP873T+b2Myu1KzjOOj6nnSN7ouHUK9cLCVs5nVhZyjFkKIGsbaSssjTQJ4pEkAO1PP8/XGZJbtSzc8d/voWWysNDQPdCe6vhftQz1pUtsNG3lUZ40gQS2EENVMi0B3WvzLnZNZF/hm0zEW70njZNYFtiWfY1vyOf6zChxtrWhbz5PoEC+iQzwJ83WWnuPVlBz6FkKIak4pReq5AjYePsOmw2fZdOTMVfca93LS0a6+J9EhhvCu7S73GjcnOfQthBB3EY1GQ5CnI0GejsS1DUKvV+xPy2HTkTPEHz7LtuRznMkrYtHuUyzabXi+dpCng2Fvu74XUfU98ZC7oVksCWohhKhhtFoNjWq50qiWKwPurc/FEj27Us8Tf/gM8UfOknA8i5SzBaScTWXu1lQ0Goj0dyE6xIt29T1pE+whHdMsiBz6FkKIu0xuYTHbks8Rf/gs8YfPkJSRazK9rGNa+xAv2gR70LiWK446Ce7KJIe+hRBCXJeznQ0dI3zpGOELQGZuIZuPGEI7/vBZk45pAFoNhPo407SOK03ruNG0ththfs7Sq/wOkaAWQoi7nI+zHd2b1aJ7s1oopUg5W0D8EUPHtF2p5zmVXWh4tnZGLj/9dQIAnbWWRrVcaVrbjaZ1XGlWx41ADwfpWV4FJKiFEEIYaTQa6no5UtfL0DENIDOnkN0nstl9PIvdJ7JIOJ5FbmEJO1LOsyPlvHFeNwebS8HtRrM6hhD3dJJbnd4uCWohhBA35ONix0ORdjwUaThUrtcrjp3NZ/eJLHYfzybheBb7T+WQVVDMuoOnWXfwtHHe2u72huC+FOCNarlIR7UKkq0lhBCiQrRaDfW8najn7UTP5oaOUBdL9BxIz2H38SwSjmez+0QWhzPzOHH+AifOX2DJnjTDvBpo4OtM09pu1PN2vHRZmQNBng4S4NchW0UIIcRts7XW0qS2G01qu/FslGFcTmEx+05kk3Aiy3DY/Hg26TmFHEjP5UB67lXL8HLSUdfTgUBPB4I8DAEe6OlAXU9H3B1s7trz3xLUQgghqoSLnQ3tQrxoF+JlHJeeXcjuE1nsO5nNsbMFpJ7NJ+VcAVkFxZzJK+JMXhF/XXbeu4yzztoQ4J4OBHo4lge6pyP+LnZotTU3xCWohRBC3DF+rnb4ufoR29DPZHx2QTEp5/JJOVtA6rkCUs7mX7opSwHpOYXkFpXw96kc/j6Vc9Uyba201Pawp66nI4EeDgR6OBDgZk9td3sC3Oyr/d64BLUQQgizc3WwoYmD4dD5lQqLSzl+zhDaKZeFeOq5Ak6cL+BiqZ6jp/M5ejr/msu2s9ES4GZPrUtDwKWh7L2fqx221pZ7TbgEtRBCCItmZ2NFqK8zob7OV00r1StOZV24FOL5pJ4t4Pj5Ak5mFXIq6wKnc4soLL5xkGs04O2ko5Z7eYAHuNoZXrsb3rvam2+vXIJaCCFEtWWl1VDHw4E6Hg60x+uq6UUlpaRnF3Iy6wInz1/g1KUAP5VteH8y6wJFJXoyc4vIzC1iV2rWNdfjYGtFgJs9jQJcmPpU8yr+VKYkqIUQQtRYOmsr45PFrkUpxbn8i5zKuhTmWRcMQX5pOJlVyJm8IgoulnI4M88s9zyXoBZCCHHX0mg0eDrp8HTS0bi26zXbFBaXkpZt2BM3x8FvCWohhBDiBuxsrAj2ciTY69p75VXNcru5XWbGjBnUrVsXOzs72rZty7Zt28xdkhBCCHFHWHxQ//jjj4wcOZK3336bnTt30rRpU2JjY8nMzDR3aUIIIUSVs/ignjJlCi+++CL9+vUjMjKSWbNm4eDgwNdff23u0oQQQogqZ9FBffHiRXbs2EFMTIxxnFarJSYmhs2bN19znqKiInJycoxDbu7V95MVQgghqguLDuozZ85QWlqKr6+vyXhfX1/S09OvOc/EiRNxdXU1DpGRkXeiVCGEEKJK1Lhe32PGjGHkyJHG98ePH6dRo0akpaWZsSohhBCiXFkm6fX6f2xr0UHt5eWFlZUVGRkZJuMzMjLw8/O75jw6nQ6dTmd8X1BQAECbNm2qrlAhhBDiFmRkZBAYGHjDNhYd1La2trRs2ZLVq1fTo0cPwPDtY/Xq1QwZMuSmltG8eXO2bduGr68vWu3tHenPzc0lMjKS/fv34+x89T1nxdVkm1WcbLOKk21WcbLNKq4yt5lerycjI4Pmzf/5dqQapZS6rbVVsR9//JE+ffrw+eef06ZNG6ZOncpPP/3EgQMHrjp3XdVycnJwdXUlOzsbFxeXO7ru6kq2WcXJNqs42WYVJ9us4sy1zSx6jxrgySef5PTp04wbN4709HSaNWvG8uXL73hICyGEEOZg8UENMGTIkJs+1C2EEELUJBZ9eZal0el0vP322yad1cSNyTarONlmFSfbrOJkm1WcubaZxZ+jFkIIIe5mskcthBBCWDAJaiGEEMKCSVALIYQQFkyCugLkudg3b+LEibRu3RpnZ2d8fHzo0aMHSUlJ5i6r2vjwww/RaDSMGDHC3KVYtJMnT/LMM8/g6emJvb09jRs35q+//jJ3WRartLSUsWPHEhwcjL29PfXr1+fdd99FuiqZWr9+Pd26dSMgIACNRsPChQtNpiulGDduHP7+/tjb2xMTE8OhQ4eqrB4J6pskz8WumHXr1jF48GC2bNnCypUrKS4uplOnTuTn55u7NIu3fft2Pv/8c5o0aWLuUiza+fPniY6OxsbGhmXLlrF//34++eQT3N3dzV2axZo0aRIzZ87ks88+IzExkUmTJvHRRx8xffp0c5dmUfLz82natCkzZsy45vSPPvqIadOmMWvWLLZu3YqjoyOxsbEUFhZWTUFK3JQ2bdqowYMHG9+XlpaqgIAANXHiRDNWVX1kZmYqQK1bt87cpVi03NxcFRoaqlauXKnuu+8+NXz4cHOXZLFGjx6t2rdvb+4yqpWuXbuq559/3mRcr169VFxcnJkqsnyAWrBggfG9Xq9Xfn5+6uOPPzaOy8rKUjqdTv3www9VUoPsUd+EW3kutjCVnZ0NgIeHh5krsWyDBw+ma9euJj9r4toWLVpEq1ateOKJJ/Dx8aF58+Z8+eWX5i7LorVr147Vq1dz8OBBAHbv3s3GjRvp0qWLmSurPpKTk0lPTzf5HXV1daVt27ZVlgfV4s5k5naj52IfOHDATFVVH3q9nhEjRhAdHU2jRo3MXY7FmjdvHjt37mT79u3mLqVaOHr0KDNnzmTkyJG88cYbbN++nWHDhmFra0ufPn3MXZ5Fev3118nJySE8PBwrKytKS0t5//33iYuLM3dp1UZ6ejrANfOgbFplk6AWVW7w4MHs27ePjRs3mrsUi3X8+HGGDx/OypUrsbOzM3c51YJer6dVq1Z88MEHgOFJefv27WPWrFkS1Nfx008/8f333zN37lwaNmxIQkICI0aMICAgQLaZBZND3zfhVp6LLQyGDBnC4sWLWbNmDbVr1zZ3ORZrx44dZGZm0qJFC6ytrbG2tmbdunVMmzYNa2trSktLzV2ixfH39ycyMtJkXEREBKmpqWaqyPK9+uqrvP766zz11FM0btyYZ599lpdffpmJEyeau7Rqo+xv/p3MAwnqm3D5c7HLlD0XOyoqyoyVWS6lFEOGDGHBggX8+eefBAcHm7ski9axY0f27t1LQkKCcWjVqhVxcXEkJCRgZWVl7hItTnR09FWX/B08eJCgoCAzVWT5CgoK0GpN/+xbWVmh1+vNVFH1ExwcjJ+fn0ke5OTksHXr1irLAzn0fZNGjhxJnz59aNWqlfG52Pn5+fTr18/cpVmkwYMHM3fuXH777TecnZ2N525cXV2xt7c3c3WWx9nZ+arz946Ojnh6esp5/et4+eWXadeuHR988AG9e/dm27ZtfPHFF3zxxRfmLs1idevWjffff5/AwEAaNmzIrl27mDJlCs8//7y5S7MoeXl5HD582Pg+OTmZhIQEPDw8CAwMZMSIEbz33nuEhoYSHBzM2LFjCQgIoEePHlVTUJX0Ja+hpk+frgIDA5Wtra1q06aN2rJli7lLsljANYfZs2ebu7RqQy7P+me///67atSokdLpdCo8PFx98cUX5i7JouXk5Kjhw4erwMBAZWdnp+rVq6fefPNNVVRUZO7SLMqaNWuu+ferT58+SinDJVpjx45Vvr6+SqfTqY4dO6qkpKQqq0eeniWEEEJYMDlHLYQQQlgwCWohhBDCgklQCyGEEBZMgloIIYSwYBLUQgghhAWToBZCCCEsmAS1EEIIYcEkqIUQQggLJkEthKh0Go2GhQsXmrsMIWoECWohapi+ffui0WiuGjp37mzu0oQQt0AeyiFEDdS5c2dmz55tMk6n05mpGiHE7ZA9aiFqIJ1Oh5+fn8ng7u4OGA5Lz5w5ky5dumBvb0+9evX45ZdfTObfu3cvDz74IPb29nh6ejJgwADy8vJM2nz99dc0bNgQnU6Hv78/Q4YMMZl+5swZevbsiYODA6GhoSxatMg47fz588TFxeHt7Y29vT2hoaFXfbEQQhhIUAtxFxo7diyPPfYYu3fvJi4ujqeeeorExEQA8vPziY2Nxd3dne3bt/Pzzz+zatUqkyCeOXMmgwcPZsCAAezdu5dFixYREhJiso4JEybQu3dv9uzZw8MPP0xcXBznzp0zrn///v0sW7aMxMREZs6ciZeX153bAEJUJ1X2XC4hhFn06dNHWVlZKUdHR5Ph/fffV0oZHkH60ksvmczTtm1bNXDgQKWUUl988YVyd3dXeXl5xulLlixRWq1WpaenK6WUCggIUG+++eZ1awDUW2+9ZXyfl5enALVs2TKllFLdunVT/fr1q5wPLEQNJ+eohaiBHnjgAWbOnGkyzsPDw/g6KirKZFpUVBQJCQkAJCYm0rRpUxwdHY3To6Oj0ev1JCUlodFoOHXqFB07drxhDU2aNDG+dnR0xMXFhczMTAAGDhzIY489xs6dO+nUqRM9evSgXbt2t/RZhajpJKiFqIEcHR2vOhRdWezt7W+qnY2Njcl7jUaDXq8HoEuXLqSkpLB06VJWrlxJx44dGTx4MJMnT670eoWo7uQctRB3oS1btlz1PiIiAoCIiAh2795Nfn6+cXp8fDxarZawsDCcnZ2pW7cuq1evvq0avL296dOnD9999x1Tp07liy++uK3lCVFTyR61EDVQUVER6enpJuOsra2NHbZ+/vlnWrVqRfv27fn+++/Ztm0b//d//wdAXFwcb7/9Nn369GH8+PGcPn2aoUOH8uyzz+Lr6wvA+PHjeemll/Dx8aFLly7k5uYSHx/P0KFDb6q+cePG0bJlSxo2bEhRURGLFy82flEQQpiSoBaiBlq+fDn+/v4m48LCwjhw4ABg6JE9b948Bg0ahL+/Pz/88AORkZEAODg4sGLFCoYPH07r1q1xcHDgscceY8qUKcZl9enTh8LCQv7zn/8watQovLy8ePzxx2+6PltbW8aMGcOxY8ewt7enQ4cOzJs3rxI+uRA1j0YppcxdhBDiztFoNCxYsIAePXqYuxQhxE2Qc9RCCCGEBZOgFkIIISyYnKMW4i4jZ7uEqF5kj1oIIYSwYBLUQgghhAWToBZCCCEsmAS1EEIIYcEkqIUQQggLJkEthBBCWDAJaiGEEMKCSVALIYQQFkyCWgghhLBg/w95Zz43LjhONQAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 500x300 with 2 Axes>"
       ]
@@ -1621,7 +1623,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x300 with 1 Axes>"
       ]
@@ -2065,7 +2067,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "TensorFlow version: 2.16.1\n",
+      "TensorFlow version: 2.15.0\n",
       "tqdm version: 4.66.2\n"
      ]
     }
@@ -2101,16 +2103,16 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "checkpoint: 100%|████████████████████████████████████████████████████| 77.0/77.0 [00:00<00:00, 118kiB/s]\n",
-      "encoder.json: 100%|███████████████████████████████████████████████| 1.04M/1.04M [00:00<00:00, 1.83MiB/s]\n",
-      "hparams.json: 100%|██████████████████████████████████████████████████| 90.0/90.0 [00:00<00:00, 101kiB/s]\n",
-      "model.ckpt.data-00000-of-00001: 100%|███████████████████████████████| 498M/498M [07:36<00:00, 1.09MiB/s]\n",
-      "model.ckpt.index: 100%|███████████████████████████████████████████| 5.21k/5.21k [00:00<00:00, 1.23MiB/s]\n",
-      "model.ckpt.meta: 100%|██████████████████████████████████████████████| 471k/471k [00:00<00:00, 1.18MiB/s]\n",
-      "vocab.bpe: 100%|████████████████████████████████████████████████████| 456k/456k [00:00<00:00, 1.74MiB/s]\n"
+      "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/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",
+      "File already exists and is up-to-date: gpt2/124M/vocab.bpe\n"
      ]
     }
    ],
@@ -2433,7 +2435,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/ch05/01_main-chapter-code/exercise-solutions.ipynb b/ch05/01_main-chapter-code/exercise-solutions.ipynb
index 1b03893..6f5eb2e 100644
--- a/ch05/01_main-chapter-code/exercise-solutions.ipynb
+++ b/ch05/01_main-chapter-code/exercise-solutions.ipynb
@@ -473,7 +473,8 @@
     "    max_length=GPT_CONFIG_124M[\"context_length\"],\n",
     "    stride=GPT_CONFIG_124M[\"context_length\"],\n",
     "    drop_last=True,\n",
-    "    shuffle=True\n",
+    "    shuffle=True,\n",
+    "    num_workers=0\n",
     ")\n",
     "\n",
     "val_loader = create_dataloader_v1(\n",
@@ -482,7 +483,8 @@
     "    max_length=GPT_CONFIG_124M[\"context_length\"],\n",
     "    stride=GPT_CONFIG_124M[\"context_length\"],\n",
     "    drop_last=False,\n",
-    "    shuffle=False\n",
+    "    shuffle=False,\n",
+    "    num_workers=0\n",
     ")"
    ]
   },
@@ -697,7 +699,8 @@
     "    max_length=GPT_CONFIG_124M[\"context_length\"],\n",
     "    stride=GPT_CONFIG_124M[\"context_length\"],\n",
     "    drop_last=True,\n",
-    "    shuffle=True\n",
+    "    shuffle=True,\n",
+    "    num_workers=0\n",
     ")\n",
     "\n",
     "val_loader = create_dataloader_v1(\n",
@@ -706,7 +709,8 @@
     "    max_length=GPT_CONFIG_124M[\"context_length\"],\n",
     "    stride=GPT_CONFIG_124M[\"context_length\"],\n",
     "    drop_last=False,\n",
-    "    shuffle=False\n",
+    "    shuffle=False,\n",
+    "    num_workers=0\n",
     ")"
    ]
   },
@@ -945,7 +949,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/ch05/01_main-chapter-code/gpt_train.py b/ch05/01_main-chapter-code/gpt_train.py
index 8382aba..b333291 100644
--- a/ch05/01_main-chapter-code/gpt_train.py
+++ b/ch05/01_main-chapter-code/gpt_train.py
@@ -7,6 +7,8 @@ import matplotlib.pyplot as plt
 import os
 import torch
 import urllib.request
+import tiktoken
+
 
 # Import from local files
 from previous_chapters import GPTModel, create_dataloader_v1, generate_text_simple
@@ -69,7 +71,7 @@ def generate_and_print_sample(model, tokenizer, device, start_context):
 
 
 def train_model_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
-                       eval_freq, eval_iter, start_context):
+                       eval_freq, eval_iter, start_context, tokenizer):
     # Initialize lists to track losses and tokens seen
     train_losses, val_losses, track_tokens_seen = [], [], []
     tokens_seen = 0
@@ -99,7 +101,7 @@ def train_model_simple(model, train_loader, val_loader, optimizer, device, num_e
 
         # Print a sample text after each epoch
         generate_and_print_sample(
-            model, train_loader.dataset.tokenizer, device, start_context
+            model, tokenizer, device, start_context
         )
 
     return train_losses, val_losses, track_tokens_seen
@@ -169,7 +171,8 @@ def main(gpt_config, settings):
         max_length=gpt_config["context_length"],
         stride=gpt_config["context_length"],
         drop_last=True,
-        shuffle=True
+        shuffle=True,
+        num_workers=0
     )
 
     val_loader = create_dataloader_v1(
@@ -178,17 +181,20 @@ def main(gpt_config, settings):
         max_length=gpt_config["context_length"],
         stride=gpt_config["context_length"],
         drop_last=False,
-        shuffle=False
+        shuffle=False,
+        num_workers=0
     )
 
     ##############################
     # Train model
     ##############################
 
+    tokenizer = tiktoken.get_encoding("gpt2")
+
     train_losses, val_losses, tokens_seen = train_model_simple(
         model, train_loader, val_loader, optimizer, device,
         num_epochs=settings["num_epochs"], eval_freq=5, eval_iter=1,
-        start_context="Every effort moves you",
+        start_context="Every effort moves you", tokenizer=tokenizer
     )
 
     return train_losses, val_losses, tokens_seen, model
diff --git a/ch05/01_main-chapter-code/previous_chapters.py b/ch05/01_main-chapter-code/previous_chapters.py
index 3da8ba1..4f2a032 100644
--- a/ch05/01_main-chapter-code/previous_chapters.py
+++ b/ch05/01_main-chapter-code/previous_chapters.py
@@ -14,12 +14,11 @@ from torch.utils.data import Dataset, DataLoader
 
 class GPTDatasetV1(Dataset):
     def __init__(self, txt, tokenizer, max_length, stride):
-        self.tokenizer = tokenizer
         self.input_ids = []
         self.target_ids = []
 
         # Tokenize the entire text
-        token_ids = self.tokenizer.encode(txt)
+        token_ids = tokenizer.encode(txt)
 
         # Use a sliding window to chunk the book into overlapping sequences of max_length
         for i in range(0, len(token_ids) - max_length, stride):
@@ -36,7 +35,7 @@ class GPTDatasetV1(Dataset):
 
 
 def create_dataloader_v1(txt, batch_size=4, max_length=256,
-                         stride=128, shuffle=True, drop_last=True):
+                         stride=128, shuffle=True, drop_last=True, num_workers=0):
     # Initialize the tokenizer
     tokenizer = tiktoken.get_encoding("gpt2")
 
@@ -45,7 +44,7 @@ def create_dataloader_v1(txt, batch_size=4, max_length=256,
 
     # Create dataloader
     dataloader = DataLoader(
-        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)
+        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=0)
 
     return dataloader
 
diff --git a/ch05/02_alternative_weight_loading/previous_chapters.py b/ch05/02_alternative_weight_loading/previous_chapters.py
index 75a3af0..c6be73f 100644
--- a/ch05/02_alternative_weight_loading/previous_chapters.py
+++ b/ch05/02_alternative_weight_loading/previous_chapters.py
@@ -14,12 +14,11 @@ from torch.utils.data import Dataset, DataLoader
 
 class GPTDatasetV1(Dataset):
     def __init__(self, txt, tokenizer, max_length, stride):
-        self.tokenizer = tokenizer
         self.input_ids = []
         self.target_ids = []
 
         # Tokenize the entire text
-        token_ids = self.tokenizer.encode(txt)
+        token_ids = tokenizer.encode(txt)
 
         # Use a sliding window to chunk the book into overlapping sequences of max_length
         for i in range(0, len(token_ids) - max_length, stride):
@@ -36,7 +35,7 @@ class GPTDatasetV1(Dataset):
 
 
 def create_dataloader_v1(txt, batch_size=4, max_length=256,
-                         stride=128, shuffle=True, drop_last=True):
+                         stride=128, shuffle=True, drop_last=True, num_workers=0):
     # Initialize the tokenizer
     tokenizer = tiktoken.get_encoding("gpt2")
 
@@ -45,7 +44,7 @@ def create_dataloader_v1(txt, batch_size=4, max_length=256,
 
     # Create dataloader
     dataloader = DataLoader(
-        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)
+        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=0)
 
     return dataloader
 
diff --git a/ch05/03_bonus_pretraining_on_gutenberg/pretraining_simple.py b/ch05/03_bonus_pretraining_on_gutenberg/pretraining_simple.py
index 8c83b1f..a4cb7de 100644
--- a/ch05/03_bonus_pretraining_on_gutenberg/pretraining_simple.py
+++ b/ch05/03_bonus_pretraining_on_gutenberg/pretraining_simple.py
@@ -15,6 +15,7 @@ import argparse
 import os
 from pathlib import Path
 import time
+import tiktoken
 import torch
 from previous_chapters import (
     create_dataloader_v1,
@@ -32,7 +33,7 @@ def read_text_file(file_path):
     return text_data
 
 
-def create_dataloaders(text_data, train_ratio, batch_size, max_length, stride):
+def create_dataloaders(text_data, train_ratio, batch_size, max_length, stride, num_workers=0):
     split_idx = int(train_ratio * len(text_data))
     train_loader = create_dataloader_v1(
         text_data[:split_idx],
@@ -40,7 +41,8 @@ def create_dataloaders(text_data, train_ratio, batch_size, max_length, stride):
         max_length=max_length,
         stride=stride,
         drop_last=True,
-        shuffle=True
+        shuffle=True,
+        num_workers=num_workers
     )
     val_loader = create_dataloader_v1(
         text_data[split_idx:],
@@ -48,7 +50,8 @@ def create_dataloaders(text_data, train_ratio, batch_size, max_length, stride):
         max_length=max_length,
         stride=stride,
         drop_last=False,
-        shuffle=False
+        shuffle=False,
+        num_workers=num_workers
     )
     return train_loader, val_loader
 
@@ -78,7 +81,7 @@ def print_eta(start_time, book_start_time, index, total_files):
 
 def train_model_simple(model, optimizer, device, n_epochs,
                        eval_freq, eval_iter, print_sample_iter, start_context,
-                       output_dir, save_ckpt_freq,
+                       output_dir, save_ckpt_freq, tokenizer,
                        batch_size=1024, train_ratio=0.90):
 
     train_losses, val_losses, track_tokens_seen = [], [], []
@@ -101,7 +104,8 @@ def train_model_simple(model, optimizer, device, n_epochs,
                     train_ratio=train_ratio,
                     batch_size=batch_size,
                     max_length=GPT_CONFIG_124M["context_length"],
-                    stride=GPT_CONFIG_124M["context_length"]
+                    stride=GPT_CONFIG_124M["context_length"],
+                    num_workers=0
                 )
                 print("Training ...")
                 model.train()
@@ -126,7 +130,7 @@ def train_model_simple(model, optimizer, device, n_epochs,
                     # Generate text passage
                     if global_step % print_sample_iter == 0:
                         generate_and_print_sample(
-                            model, train_loader.dataset.tokenizer, device, start_context
+                            model, tokenizer, device, start_context
                         )
 
                 if global_step % save_ckpt_freq:
@@ -196,6 +200,7 @@ if __name__ == "__main__":
     model = GPTModel(GPT_CONFIG_124M)
     model.to(device)
     optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=0.1)
+    tokenizer = tiktoken.get_encoding("gpt2")
 
     data_dir = args.data_dir
     all_files = [os.path.join(path, name) for path, subdirs, files
@@ -221,6 +226,7 @@ if __name__ == "__main__":
         output_dir=output_dir,
         save_ckpt_freq=args.save_ckpt_freq,
         start_context="Every effort moves you",
+        tokenizer=tokenizer
     )
 
     epochs_tensor = torch.linspace(0, args.n_epochs, len(train_losses))
diff --git a/ch05/03_bonus_pretraining_on_gutenberg/previous_chapters.py b/ch05/03_bonus_pretraining_on_gutenberg/previous_chapters.py
index 5e22a26..b79348d 100644
--- a/ch05/03_bonus_pretraining_on_gutenberg/previous_chapters.py
+++ b/ch05/03_bonus_pretraining_on_gutenberg/previous_chapters.py
@@ -21,11 +21,10 @@ import matplotlib.pyplot as plt
 
 class GPTDatasetV1(Dataset):
     def __init__(self, txt, tokenizer, max_length, stride):
-        self.tokenizer = tokenizer
         self.input_ids = []
         self.target_ids = []
 
-        token_ids = self.tokenizer.encode(txt, allowed_special={'<|endoftext|>'})
+        token_ids = tokenizer.encode(txt, allowed_special={'<|endoftext|>'})
 
         for i in range(0, len(token_ids) - max_length, stride):
             input_chunk = token_ids[i:i + max_length]
@@ -41,11 +40,11 @@ class GPTDatasetV1(Dataset):
 
 
 def create_dataloader_v1(txt, batch_size=4, max_length=256,
-                         stride=128, shuffle=True, drop_last=True):
+                         stride=128, shuffle=True, drop_last=True, num_workers=0):
     tokenizer = tiktoken.get_encoding("gpt2")
     dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
     dataloader = DataLoader(
-        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)
+        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=0)
 
     return dataloader
 
diff --git a/ch05/05_bonus_hparam_tuning/hparam_search.py b/ch05/05_bonus_hparam_tuning/hparam_search.py
index b50a079..c207357 100644
--- a/ch05/05_bonus_hparam_tuning/hparam_search.py
+++ b/ch05/05_bonus_hparam_tuning/hparam_search.py
@@ -6,6 +6,7 @@
 import itertools
 import math
 import os
+import tiktoken
 import torch
 from previous_chapters import GPTModel, create_dataloader_v1
 
@@ -58,7 +59,7 @@ def evaluate_model(model, train_loader, val_loader, device, eval_iter):
 
 def train_model(model, train_loader, val_loader, optimizer, device,
                 n_epochs, eval_freq, eval_iter,
-                encoded_start_context, warmup_iters=10,
+                encoded_start_context, tokenizer, warmup_iters=10,
                 initial_lr=3e-05, min_lr=1e-6):
     global_step = 0
 
@@ -120,6 +121,7 @@ if __name__ == "__main__":
     with open(os.path.join(script_dir, "the-verdict.txt"), "r", encoding="utf-8") as file:
         text_data = file.read()
 
+    tokenizer = tiktoken.get_encoding("gpt2")
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     train_ratio = 0.95
@@ -155,7 +157,8 @@ if __name__ == "__main__":
                 max_length=GPT_CONFIG_124M["context_length"],
                 stride=GPT_CONFIG_124M["context_length"],
                 drop_last=True,
-                shuffle=True
+                shuffle=True,
+                num_workers=0
             )
 
             val_loader = create_dataloader_v1(
@@ -164,7 +167,8 @@ if __name__ == "__main__":
                 max_length=GPT_CONFIG_124M["context_length"],
                 stride=GPT_CONFIG_124M["context_length"],
                 drop_last=False,
-                shuffle=False
+                shuffle=False,
+                num_workers=0
             )
 
             model = GPTModel(GPT_CONFIG_124M)
@@ -176,7 +180,7 @@ if __name__ == "__main__":
                 weight_decay=HPARAM_CONFIG["weight_decay"]
             )
 
-            encoded_start_context = train_loader.dataset.tokenizer.encode("Nevertheless")
+            encoded_start_context = tokenizer.encode("Nevertheless")
             encoded_tensor = torch.tensor(encoded_start_context).unsqueeze(0)
 
             train_loss, val_loss = train_model(
@@ -184,6 +188,7 @@ if __name__ == "__main__":
                 n_epochs=HPARAM_CONFIG["n_epochs"],
                 eval_freq=5, eval_iter=1,
                 encoded_start_context=encoded_tensor,
+                tokenizer=tokenizer,
                 warmup_iters=HPARAM_CONFIG["warmup_iters"],
                 initial_lr=HPARAM_CONFIG["initial_lr"],
                 min_lr=HPARAM_CONFIG["min_lr"]
diff --git a/ch05/05_bonus_hparam_tuning/previous_chapters.py b/ch05/05_bonus_hparam_tuning/previous_chapters.py
index c5c6c1c..2fd2d9c 100644
--- a/ch05/05_bonus_hparam_tuning/previous_chapters.py
+++ b/ch05/05_bonus_hparam_tuning/previous_chapters.py
@@ -19,12 +19,11 @@ from torch.utils.data import Dataset, DataLoader
 
 class GPTDatasetV1(Dataset):
     def __init__(self, txt, tokenizer, max_length, stride):
-        self.tokenizer = tokenizer
         self.input_ids = []
         self.target_ids = []
 
         # Tokenize the entire text
-        token_ids = self.tokenizer.encode(txt)
+        token_ids = tokenizer.encode(txt)
 
         # Use a sliding window to chunk the book into overlapping sequences of max_length
         for i in range(0, len(token_ids) - max_length, stride):
@@ -46,11 +45,11 @@ def create_dataloader_v1(txt, batch_size=4, max_length=256,
     tokenizer = tiktoken.get_encoding("gpt2")
 
     # Create dataset
-    dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
+    dataset = GPTDatasetV1(txt, tokenizer, max_length, stride, num_workers=0)
 
     # Create dataloader
     dataloader = DataLoader(
-        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)
+        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=0)
 
     return dataloader