Update UI

app.py

@@ -235,7 +235,7 @@ with gr.Blocks(css=css, theme=gr.themes.Soft(text_size='lg', font=["monospace"],
     gr.Markdown('''
             This space generates a **Jupyter Notebook file (.ipynb)** 📔⚙️ that guides you through the 
             entire process of **supervised fine-tuning** of a raw Large Language Model (**LLM**) 🧠 on a chosen dataset in 
-            the **Conversational format**. The process is facilitated by an intuitive **User Interface (UI)** 👆💻:
+            the **Conversational format**. The process is facilitated by an intuitive **User Interface (UI)** 👆💻**:**
             ''', elem_classes=["center_text"])
     with dashed_row():
         with centered_column():

utils/notebook_generator.py

@@ -37,7 +37,7 @@ except ImportError:
 
 def create_install_flash_attention(cells: list):
     text_cell = nbf.v4.new_markdown_cell(
-        "# Installing Flash Attention")
+        "## Installing Flash Attention")
     text_cell1 = nbf.v4.new_markdown_cell("Installing Flash Attention to reduce the memory "
                                           "and runtime cost of the attention layer, and improve the performance of "
                                           "the model training. Learn more at [FlashAttention]("
@@ -59,7 +59,7 @@ import torch; assert torch.cuda.get_device_capability()[0] >= 8, 'Hardware not s
 
 def create_login_hf_cells(cells: list, should_login: bool = False, model_name: Optional[str] = None):
     text_cell = nbf.v4.new_markdown_cell(
-        "# Login to HF")
+        "## Login to HF")
 
     text_1 = "Login with our `HF_TOKEN` in order to push the finetuned model to `huggingface_hub`."
 
@@ -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 LoraConfig")
     code = f"""
 from peft import LoraConfig
 
@@ -249,7 +249,7 @@ def create_training_args_cells(cells: list, epochs, max_steps, logging_steps, pe
                                save_strategy, gradient_accumulation_steps, gradient_checkpointing,
                                learning_rate, max_grad_norm, warmup_ratio, lr_scheduler_type, output_dir,
                                report_to, seed):
-    text_cell = nbf.v4.new_markdown_cell("# Setting the TrainingArguments")
+    text_cell = nbf.v4.new_markdown_cell("## Setting the TrainingArguments")
     to_install = None
     if report_to == "all":
         to_install = "azure_ml comet_ml mlflow tensorboard wandb"
@@ -298,7 +298,7 @@ args = TrainingArguments(
 
 def create_sft_trainer_cells(cells: list, max_seq_length, packing):
     text_cell = nbf.v4.new_markdown_cell(
-        """# Setting the Supervised Finetuning Trainer (`SFTTrainer`)
+        """## Setting the Supervised Finetuning Trainer (`SFTTrainer`)
     
 This `SFTTrainer` is a wrapper around the `transformers.Trainer` class and inherits all of its attributes and methods.
 The trainer takes care of properly initializing the `PeftModel`.   
@@ -362,7 +362,7 @@ trainer.save_model()
 
 def create_free_gpu_cells(cells: list):
     text_cell = nbf.v4.new_markdown_cell(
-        """# Free the GPU Memory to Prepare Merging Lora Adapters with the Base Model
+        """### Free the GPU Memory to Prepare Merging `LoRA` Adapters with the Base Model
 """)
 
     code = f"""
@@ -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 
@@ -411,7 +411,7 @@ tokenizer.save_pretrained("{output_dir}")
 
 def merge_model_cells(cells: list, output_dir):
     text_cell = nbf.v4.new_markdown_cell(
-        f"# Copy all result folders from 'temp_{output_dir}' to '{output_dir}'")
+        f"### Copy all result folders from 'temp_{output_dir}' to '{output_dir}'")
 
     code = f"""
 import os
@@ -433,7 +433,7 @@ for item in os.listdir(source_folder):
 
 
 def push_to_hub_cells(cells: list, output_dir):
-    text = f"# Pushing '{output_dir}' to our Hugging Face account."
+    text = f"## Pushing '{output_dir}' to our Hugging Face account."
     code = f"""
 from huggingface_hub import HfApi, HfFolder, Repository