added markdown title per gradio row

app.py

@@ -67,6 +67,7 @@ def scrape_config_from_hub(repo_id):
 
 def build_interface(estimate_vram_fn):
     with gr.Blocks() as app:
+        gr.Markdown("## Select either an existing HF model from a repository or choose your own model parameters")
         option = gr.Radio(["Repo ID", "Model Parameters"], label="Select Input Type")
         repo_id = gr.Textbox(label="Repo ID", visible=False, placeholder="mistralai/Mistral-7B-v0.1")
 
@@ -91,7 +92,7 @@ def build_interface(estimate_vram_fn):
                 outputs=[repo_id, model_params_row]
             )
         
-        
+        gr.Markdown("## Select training parameters")
         with gr.Row(equal_height=True):
             training_params = [gr.Dropdown(label="Micro batch size", choices=BATCH_SIZES, value=4, info="Micro batch size (batch size per device/GPU)"),
                                gr.Dropdown(label="ZeRO stage", choices=ZERO_STAGES, value=0, info="ZeRO optimization stage"),
@@ -100,7 +101,7 @@ def build_interface(estimate_vram_fn):
                                gr.Dropdown(label="Optimizer", choices=OPTIMIZERS, value="adamw", info="Type of optimizer"),
                                gr.Dropdown(label="QLoRA", choices=[False, True], value=False, info="Finetune with QLoRA enabled"),
                                gr.Slider(label="Num GPUs", minimum=1, maximum=64, step=1, value=4, info="Number of GPUs. Necessary for estimating ZeRO stages"),
-                               gr.Textbox(label="Cache dir", value=None, placeholder=".huggingface_configs", info="HuggingFace cache directory to download config from")
+                               gr.Textbox(label="Cache dir", value=None, placeholder=".huggingface_configs", info="HuggingFace cache directory to download config to")
             ]
 
         submit_btn = gr.Button("Estimate!")