more compact layout

app.py

@@ -92,9 +92,7 @@ TITLE = """
 # for some reason https://huggingface.co/settings/tokens is not showing as a link by default?
 DESCRIPTION = """
 This Space allows to automatically convert to ONNX 🤗 transformers PyTorch models hosted on the Hugging Face Hub. It opens a PR on the target model, and it is up to the owner of the original model
-to merge the PR to allow people to leverage the ONNX standard to share and use the model on a wide range of devices!
-
-Once converted, the model can for example be used in the [🤗 Optimum](https://huggingface.co/docs/optimum/) library following closely the transormers API.
+to merge the PR to allow people to leverage the ONNX standard to share and use the model on a wide range of devices! Once converted, the model can for example be used in the [🤗 Optimum](https://huggingface.co/docs/optimum/) library following closely the transormers API.
 Check out [this guide](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models) to see how!
 
 The steps are the following:
@@ -102,8 +100,6 @@ The steps are the following:
 - Input a model id from the Hub (for example: [textattack/distilbert-base-cased-CoLA](https://huggingface.co/textattack/distilbert-base-cased-CoLA))
 - Click "Convert to ONNX"
 - That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR!
-
-Note: in case the model to convert is larger than 2 GB, it will be saved in a subfolder called `onnx/`. To load it from Optimum, the argument `subfolder="onnx"` should be provided.
 """
 
 with gr.Blocks() as demo:
@@ -127,6 +123,10 @@ with gr.Blocks() as demo:
         btn = gr.Button("Convert to ONNX")
         output = gr.Markdown(label="Output")
 
+        gr.Markdown("""
+Note: in case the model to convert is larger than 2 GB, it will be saved in a subfolder called `onnx/`. To load it from Optimum, the argument `subfolder="onnx"` should be provided.
+        """)
+
     btn.click(
         fn=onnx_export, inputs=[input_token, input_model, input_task], outputs=output
     )