Update app.py

app.py

@@ -127,11 +127,11 @@ demo = gr.ChatInterface(
     description="The model may take a while if it hasn't run recently or a lot of people are using it",
     title="EliGPT v1.3",
     additional_inputs=[
-        gr.Radio(presets.keys(), label="Preset", info="Gaslight the model into acting a certain way - WARNING, IF YOU CHANGE THIS WHILE THERE ARE MESSAGES IN THE CHAT, THE MODEL WILL BECOME VERY SLOW FOR YOU", value="none"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=1.0, step=0.1, label="Temperature", info="How chaotic should the model be?"),
-        gr.Slider(minimum=0.0, maximum=1.0, value=0.15, step=0.01, label="Min_p", info="Lower values give it more \"personality\""),
-        gr.Slider(minimum=0, maximum=512, value=32, step=1, label="Length penalty start", info='When should the model start being more likely to shut up?'),
-        gr.Slider(minimum=0.5, maximum=1.5, value=1.02, step=0.01, label="Length penalty decay factor", info='How fast should that stop likelihood increase?'),
+        gr.Radio(presets.keys(), label="Preset", info="Gaslight the model into acting a certain way - WARNING, IF YOU CHANGE THIS WHILE THERE ARE MESSAGES IN THE CHAT, THE MODEL WILL BECOME VERY SLOW FOR YOU", value="emojis"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=1.5, step=0.1, label="Temperature", info="How chaotic should the model be?"),
+        gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Min_p", info="Lower values give it more \"personality\""),
+        gr.Slider(minimum=0, maximum=512, value=5, step=1, label="Length penalty start", info='When should the model start being more likely to shut up?'),
+        gr.Slider(minimum=0.5, maximum=1.5, value=1.005, step=0.001, label="Length penalty decay factor", info='How fast should that stop likelihood increase?'),
         gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Frequency penalty", info='"Don\'repeat yourself"'),
         gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Presence penalty", info='"Use lots of diverse words"'),
         gr.Slider(minimum=1, maximum=1024, value=1024, step=1, label="Max new tokens", info="How many words can the model generate at most?"),