diff --git a/ch02/05_bpe-from-scratch/tests.py b/ch02/05_bpe-from-scratch/tests.py
index e82e8cd..254c614 100644
--- a/ch02/05_bpe-from-scratch/tests.py
+++ b/ch02/05_bpe-from-scratch/tests.py
@@ -5,8 +5,6 @@ import nbformat
 import types
 import pytest
 
-import tiktoken
-
 
 def import_definitions_from_notebook(fullname, names):
     """Loads function definitions from a Jupyter notebook file into a module."""
@@ -47,17 +45,15 @@ def verdict_file(imported_module):
     """Fixture to handle downloading The Verdict file."""
     download_file_if_absent = getattr(imported_module, "download_file_if_absent", None)
 
-    verdict_path = download_file_if_absent(
-        url=(
-            "https://raw.githubusercontent.com/rasbt/"
-            "LLMs-from-scratch/main/ch02/01_main-chapter-code/"
-            "the-verdict.txt"
-        ),
-        filename="the-verdict.txt",
-        search_dirs=["ch02/01_main-chapter-code/", "../01_main-chapter-code/", "."]
-    )
+    search_directories = [".", "../02_bonus_bytepair-encoder/gpt2_model/"]
+    files_to_download = {
+        "https://openaipublic.blob.core.windows.net/gpt-2/models/124M/vocab.bpe": "vocab.bpe",
+        "https://openaipublic.blob.core.windows.net/gpt-2/models/124M/encoder.json": "encoder.json"
+    }
+    paths = {filename: download_file_if_absent(url, filename, search_directories)
+             for url, filename in files_to_download.items()}
 
-    return verdict_path
+    return paths
 
 
 @pytest.fixture(scope="module")
@@ -98,6 +94,7 @@ def test_tokenizer_training(imported_module, verdict_file):
     tokenizer2.load_vocab_and_merges(vocab_path="vocab.json", bpe_merges_path="bpe_merges.txt")
     assert tokenizer2.decode(token_ids) == input_text, "Decoded text mismatch after reloading tokenizer."
 
+
 def test_gpt2_tokenizer_openai_simple(imported_module, gpt2_files):
     BPETokenizerSimple = getattr(imported_module, "BPETokenizerSimple", None)
 
diff --git a/ch05/10_llm-training-speed/02_opt_multi_gpu_ddp.py b/ch05/10_llm-training-speed/02_opt_multi_gpu_ddp.py
index 29db397..746cc7f 100644
--- a/ch05/10_llm-training-speed/02_opt_multi_gpu_ddp.py
+++ b/ch05/10_llm-training-speed/02_opt_multi_gpu_ddp.py
@@ -312,7 +312,7 @@ def generate_and_print_sample(model, device, start_context):
 
 
 def train_model_simple_with_timing(model, train_loader, val_loader, optimizer, device,
-                                   num_epochs, eval_freq, eval_iter, start_context, tokenizer):
+                                   num_epochs, eval_freq, eval_iter, start_context):
     train_losses, val_losses, track_tokens = [], [], []
     total_tokens, global_step, last_tokens = 0, -1, 0
 
@@ -524,8 +524,6 @@ def main(gpt_config, settings, rank, world_size):
     # Train model
     ##############################
 
-    tokenizer = tiktoken.get_encoding("gpt2")
-
     train_losses, val_losses, tokens_seen = train_model_simple_with_timing(
         model=model,
         train_loader=train_loader,
@@ -536,7 +534,6 @@ def main(gpt_config, settings, rank, world_size):
         eval_freq=5,
         eval_iter=1,
         start_context="Every effort moves you",
-        tokenizer=tokenizer
     )
 
     # NEW: Clean up distributed processes
diff --git a/ch06/01_main-chapter-code/gpt_class_finetune.py b/ch06/01_main-chapter-code/gpt_class_finetune.py
index 8308304..239f374 100644
--- a/ch06/01_main-chapter-code/gpt_class_finetune.py
+++ b/ch06/01_main-chapter-code/gpt_class_finetune.py
@@ -175,7 +175,7 @@ def evaluate_model(model, train_loader, val_loader, device, eval_iter):
 
 
 def train_classifier_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
-                            eval_freq, eval_iter, tokenizer):
+                            eval_freq, eval_iter):
     # Initialize lists to track losses and tokens seen
     train_losses, val_losses, train_accs, val_accs = [], [], [], []
     examples_seen, global_step = 0, -1
@@ -408,7 +408,6 @@ if __name__ == "__main__":
     train_losses, val_losses, train_accs, val_accs, examples_seen = train_classifier_simple(
         model, train_loader, val_loader, optimizer, device,
         num_epochs=num_epochs, eval_freq=50, eval_iter=5,
-        tokenizer=tokenizer
     )
 
     end_time = time.time()