Update app.py

app.py

@@ -65,10 +65,36 @@ with gr.Blocks() as demo:
     with gr.Tabs():
         # Memory Calculation Tab
         with gr.TabItem("Memory Calculation"):
-            hf_model_name_or_path = gr.Textbox(
-                label="HuggingFace Model Name or Path",
-                info="Name of the HuggingFace Hub repository or the local file path for it"
-            )
+            with gr.group():
+                hf_model_name_or_path = gr.Textbox(
+                    label="HuggingFace Model Name or Path",
+                    info="Name of the HuggingFace Hub repository or the local file path for it"
+                )
+                sequence_length = gr.Number(
+                    label="Sequence Length", 
+                    value=2048, 
+                    info="Sequence length used for training"
+                )
+                vocab_size = gr.Number(
+                    label="Vocab Size", 
+                    value=51200, 
+                    info="How many tokens are in the embedding layer"
+                )
+                hidden_size = gr.Number(
+                    label="Hidden Size", 
+                    value=6144, 
+                    info="Dimension of the model's hidden size"
+                )
+                num_attention_heads = gr.Number(
+                    label="Number of Attention Heads", 
+                    value=64, 
+                    info="Number of attention heads used in the model"
+                )
+                num_layers = gr.Number(
+                    label="Number of Layers", 
+                    value=44, 
+                    info="Number of transformer layers used in the model"
+                )            
             num_gpus = gr.Number(
                 label="Number of GPUs", 
                 value=1, 
@@ -89,31 +115,6 @@ with gr.Blocks() as demo:
                 value=8, 
                 info="Batch size per GPU"
             )
-            sequence_length = gr.Number(
-                label="Sequence Length", 
-                value=2048, 
-                info="Sequence length used for training"
-            )
-            vocab_size = gr.Number(
-                label="Vocab Size", 
-                value=51200, 
-                info="How many tokens are in the embedding layer"
-            )
-            hidden_size = gr.Number(
-                label="Hidden Size", 
-                value=6144, 
-                info="Dimension of the model's hidden size"
-            )
-            num_attention_heads = gr.Number(
-                label="Number of Attention Heads", 
-                value=64, 
-                info="Number of attention heads used in the model"
-            )
-            num_layers = gr.Number(
-                label="Number of Layers", 
-                value=44, 
-                info="Number of transformer layers used in the model"
-            )
             ffn_expansion_factor = gr.Number(
                 label="FFN Expansion Factor", 
                 value=4, 
@@ -148,35 +149,36 @@ with gr.Blocks() as demo:
 
         # Parameter Calculation Tab
         with gr.TabItem("Parameter Calculation"):
-            hf_model_name_or_path = gr.Textbox(
-                label="HuggingFace Model Name or Path",
-                info="Name of the HuggingFace Hub repository or the local file path for it"
-            )
-            vocab_size = gr.Number(
-                label="Vocab Size", 
-                value=51200, 
-                info="How many tokens are in the embedding layer"
-            )
+            with gr.group():
+                hf_model_name_or_path = gr.Textbox(
+                    label="HuggingFace Model Name or Path",
+                    info="Name of the HuggingFace Hub repository or the local file path for it"
+                )
+                vocab_size = gr.Number(
+                    label="Vocab Size", 
+                    value=51200, 
+                    info="How many tokens are in the embedding layer"
+                )
+                hidden_size = gr.Number(
+                    label="Hidden Size", 
+                    value=6144, 
+                    info="Dimension of the model's hidden size"
+                )
+                sequence_length = gr.Number(
+                    label="Sequence Length", 
+                    value=2048, 
+                    info="Sequence length used for training"
+                )
+                num_layers = gr.Number(
+                    label="Number of Layers", 
+                    value=44, 
+                    info="Number of transformer layers used in the model"
+                )
             tied_embeddings = gr.Checkbox(
                 label="Tied Embeddings", 
                 value=False, 
                 info="Whether embeddings are tied (shared between input and output)"
             )
-            hidden_size = gr.Number(
-                label="Hidden Size", 
-                value=6144, 
-                info="Dimension of the model's hidden size"
-            )
-            sequence_length = gr.Number(
-                label="Sequence Length", 
-                value=2048, 
-                info="Sequence length used for training"
-            )
-            num_layers = gr.Number(
-                label="Number of Layers", 
-                value=44, 
-                info="Number of transformer layers used in the model"
-            )
             ffn_expansion_factor = gr.Number(
                 label="FFN Expansion Factor", 
                 value=4,