diff --git a/ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb b/ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb
index 7561bf6..0f92a87 100644
--- a/ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb
+++ b/ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb
@@ -172,7 +172,7 @@
     "def assign_check(left, right):\n",
     "    if left.shape != right.shape:\n",
     "        raise ValueError(f\"Shape mismatch. Left: {left.shape}, Right: {right.shape}\")\n",
-    "    return torch.nn.Parameter(torch.tensor(right))"
+    "    return torch.nn.Parameter(right.clone().detach())"
    ]
   },
   {
@@ -227,16 +227,7 @@
    "execution_count": 7,
    "id": "cda44d37-92c0-4c19-a70a-15711513afce",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_9385/3877979348.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
-      "  return torch.nn.Parameter(torch.tensor(right))\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "from previous_chapters import GPTModel\n",
@@ -250,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "4ddd0d51-3ade-4890-9bab-d63f141d095f",
    "metadata": {},
    "outputs": [
@@ -302,7 +293,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
diff --git a/ch06/02_bonus_additional-experiments/additional-experiments.py b/ch06/02_bonus_additional-experiments/additional-experiments.py
index f3217ed..1ba1ae5 100644
--- a/ch06/02_bonus_additional-experiments/additional-experiments.py
+++ b/ch06/02_bonus_additional-experiments/additional-experiments.py
@@ -117,7 +117,7 @@ def random_split(df, train_frac, validation_frac):
     return train_df, validation_df, test_df
 
 
-def create_dataset_csvs(data_file_path):
+def create_dataset_csvs(new_file_path):
     df = pd.read_csv(new_file_path, sep="\t", header=None, names=["Label", "Text"])
 
     # Create balanced dataset