Improve comments

app.py

@@ -304,7 +304,7 @@ with gr.Blocks(css=css, theme=gr.themes.Soft(text_size='lg', font=["monospace"],
     with gr.Row():
         all_components.update(add_pad_tokens())
 
-    gr.HTML("<h2 style='text-align: center;'>Lora Configuration</h2>")
+    gr.HTML("<h2 style='text-align: center;'>LoRA Configuration</h2>")
     with gr.Row():
         with centered_column():
             all_components.update(add_lora_components1())

utils/notebook_generator.py

@@ -219,7 +219,7 @@ a 24GB GPU for fine-tuning.
 
 
 def create_lora_config_cells(cells: list, r: int, alpha: int, dropout: float, bias: str):
-    text_cell = nbf.v4.new_markdown_cell("## Setting LoraConfig")
+    text_cell = nbf.v4.new_markdown_cell("## Setting LoRA Config")
     code = f"""
 from peft import LoraConfig
 
@@ -379,7 +379,7 @@ torch.cuda.empty_cache()
 
 def create_merge_lora_cells(cells: list, output_dir):
     text_cell = nbf.v4.new_markdown_cell(
-        """## Merging LoRa Adapters into the Original Model
+        """## Merging LoRA Adapters into the Original Model
 
 While utilizing `LoRA`, we focus on training the adapters rather than the entire model. Consequently, during the 
 model saving process, only the `adapter weights` are preserved, not the complete model. If we wish to save the