Update app.py

app.py

@@ -6,7 +6,7 @@ import torch
 import gradio as gr
 import sentencepiece
 
-title = "Welcome to 🙋🏻‍♂️Tonic's🌷Tulu Chat!"
+title = "# ²Welcome to 🙋🏻‍♂️Tonic's🌷Tulu Chat!"
 description = """[allenai/tulu-2-dpo-7b](https://huggingface.co/allenai/tulu-2-dpo-7b) and larger Tulu-2 models are Instruct Llama Finetunes using the [mistralai/Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) recipe. You can use [allenai/tulu-2-13b](https://huggingface.co/allenai/tulu-2-13b) here via API using Gradio by scrolling down and clicking Use 'Via API' or privately by [cloning this space on huggingface](https://huggingface.co/spaces/Tonic1/TuluDemo?duplicate=true) See also the large model here : [allenai/tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b) . [Join my active builders' server on discord](https://discord.gg/VqTxc76K3u). Let's build together!. [Add this Space as a discord bot to your server by clicking this link](https://discord.com/oauth2/authorize?client_id=1176628808212828231&scope=bot+applications.commands&permissions=326417525824). Big thanks to 🤗Huggingface Organisation for the🫂Community Grant"""
 
 os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:50'
@@ -51,7 +51,7 @@ class TuluChatBot:
 def gradio_predict(user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty, do_sample):
     Tulu_bot.set_system_message(system_message)
     if not do_sample:
-        max_length = 1269
+        max_length = 780
         temperature = 1.2  
         top_p = 0.9        
         repetition_penalty = 0.9  
@@ -72,7 +72,7 @@ with gr.Blocks(theme = "ParityError/Anime") as demo:
     
     with gr.Accordion("Advanced Settings", open=lambda do_sample: do_sample):
         with gr.Row():
-            max_new_tokens = gr.Slider(label="Max new tokens", value=1269, minimum=550, maximum=3200, step=1)
+            max_new_tokens = gr.Slider(label="Max new tokens", value=780, minimum=550, maximum=3200, step=1)
             temperature = gr.Slider(label="Temperature", value=0.3, minimum=0.1, maximum=1.0, step=0.1)
             top_p = gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.01, maximum=0.99, step=0.05)
             repetition_penalty = gr.Slider(label="Repetition penalty", value=1.9, minimum=1.0, maximum=2.0, step=0.05)