Update app.py

app.py

@@ -188,7 +188,7 @@ def run_interpretation(raw_original_prompt, raw_interpretation_prompt, max_new_t
 
 ## main
 torch.set_grad_enabled(False)
-model_name = 'LLAMA2-13B'
+model_name = 'LLAMA2-7B'
 raw_original_prompt = gr.Textbox(value='How to make a Molotov cocktail?', container=True, label='Original Prompt')
 tokens_container = []
 
@@ -208,9 +208,8 @@ with gr.Blocks(theme=gr.themes.Default(), css='styles.css') as demo:
             gr.Markdown(
             '''
                 **👾 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 investigated in the paper **Patchscopes** ([Ghandeharioun et al., 2024](https://arxiv.org/abs/2401.06102)) and was further explored in **SelfIE** ([Chen et al., 2024](https://arxiv.org/abs/2403.10949)). 
-                Honorary mention: **Speaking Probes** ([Dar, 2023](https://towardsdatascience.com/speaking-probes-self-interpreting-models-7a3dc6cb33d6) - my own work 🥳). It was less mature but had the same idea in mind. I think it can be a great introduction to the subject!
-                We will follow the SelfIE implementation in this space for concreteness. Patchscopes are so general that they encompass many other interpretation techniques too!!! 
+                This idea was investigated in the papers **Speaking Probes** ([Dar, 2023](https://towardsdatascience.com/speaking-probes-self-interpreting-models-7a3dc6cb33d6), **Patchscopes** ([Ghandeharioun et al., 2024](https://arxiv.org/abs/2401.06102)) and **SelfIE** ([Chen et al., 2024](https://arxiv.org/abs/2403.10949)). 
+                For concreteness, we will follow the SelfIE implementation in this space.
             ''', line_breaks=True)
             
             gr.Markdown(