Update app.py

app.py

@@ -147,18 +147,17 @@ with gr.Blocks(theme=gr.themes.Default(), css=css) as demo:
     global_state = gr.State([])
     with gr.Row():
         with gr.Column(scale=5):
-            gr.Markdown('''
-                # 😎 Self-Interpreting Models
-                
-                👾 **This space is a simple introduction to the emerging trend of models interpreting their _own hidden states_ in free form natural language**!! 👾
-                This idea was explored in the paper **Patchscopes** ([Ghandeharioun et al., 2024](https://arxiv.org/abs/2401.06102)) and was later investigated further in **SelfIE** ([Chen et al., 2024](https://arxiv.org/abs/2403.10949)). 
-                An honorary mention of **Speaking Probes** ([Dar, 2023](https://towardsdatascience.com/speaking-probes-self-interpreting-models-7a3dc6cb33d6) -- my own work!! 🥳) which was less mature but had the same idea in mind. 
-                We will follow the SelfIE implementation in this space for concreteness. Patchscopes are so general that they encompass many other interpretation techniques too!!! 
-                
-                👾 **The idea is really simple: models are able to understand their own hidden states by nature!** 👾  
-                If I give a model a prompt of the form ``User: [X] Assistant: Sure'll I'll repeat your message`` and replace ``[X]`` *during computation* with the hidden state we want to understand, 
-                we hope to get back a summary of the information that exists inside the hidden state, because it is encoded in a latent space the model uses itself!! How cool is that! 😯😯😯
-                ''', line_breaks=True)
+            gr.Markdown('# 😎 Self-Interpreting Models')
+            with gr.Accordion(label='👾 **This space is a simple introduction to the emerging trend of models interpreting their _own hidden states_ in free form natural language**!! 👾'):
+                gr.Markdown(
+                '''This idea was explored in the paper **Patchscopes** ([Ghandeharioun et al., 2024](https://arxiv.org/abs/2401.06102)) and was later investigated further in **SelfIE** ([Chen et al., 2024](https://arxiv.org/abs/2403.10949)). 
+                    An honorary mention of **Speaking Probes** ([Dar, 2023](https://towardsdatascience.com/speaking-probes-self-interpreting-models-7a3dc6cb33d6) -- my own work!! 🥳) which was less mature but had the same idea in mind. 
+                    We will follow the SelfIE implementation in this space for concreteness. Patchscopes are so general that they encompass many other interpretation techniques too!!! 
+                    
+                    👾 **The idea is really simple: models are able to understand their own hidden states by nature!** 👾  
+                    If I give a model a prompt of the form ``User: [X] Assistant: Sure'll I'll repeat your message`` and replace ``[X]`` *during computation* with the hidden state we want to understand, 
+                    we hope to get back a summary of the information that exists inside the hidden state, because it is encoded in a latent space the model uses itself!! How cool is that! 😯😯😯
+                    ''', line_breaks=True)
         with gr.Column(scale=1):    
             gr.Markdown('<span style="font-size:180px;">🤔</span>')