Elijahbodden commited on
Commit
47e8d89
·
verified ·
1 Parent(s): d2a0386

Update app.py

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Files changed (1) hide show
  1. app.py +1 -1
app.py CHANGED
@@ -131,7 +131,7 @@ demo = gr.ChatInterface(
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  gr.Slider(minimum=0.1, maximum=4.0, value=0.8, step=0.1, label="Temperature", info="How chaotic should the model be?"),
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  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?'),
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  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?'),
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- gr.Slider(minimum=0.0, maximum=10.0, value=3.0, step=0.5, label="Min_p", info="Lower values make it more random (ratio between lowest-probability and highest-probability tokens)"),
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  gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Frequency penalty", info='"Don\'repeat yourself"'),
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  gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Presence penalty", info='"Use lots of diverse words"'),
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  gr.Slider(minimum=1, maximum=1024, value=1024, step=1, label="Max new tokens", info="How many words can the model generate at most?"),
 
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  gr.Slider(minimum=0.1, maximum=4.0, value=0.8, step=0.1, label="Temperature", info="How chaotic should the model be?"),
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  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?'),
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  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?'),
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+ gr.Slider(minimum=0.0, maximum=1.0, value=0.15, step=0.01, label="Min_p", info="Lower values make it more random (ratio between lowest-probability and highest-probability tokens)"),
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  gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Frequency penalty", info='"Don\'repeat yourself"'),
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  gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Presence penalty", info='"Use lots of diverse words"'),
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  gr.Slider(minimum=1, maximum=1024, value=1024, step=1, label="Max new tokens", info="How many words can the model generate at most?"),