Update app.py
Browse files
app.py
CHANGED
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@@ -60,16 +60,22 @@ with demo:
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gr.Markdown("## UGI Leaderboard", elem_classes="text-lg text-center")
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gr.Markdown("""
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UGI: Uncensored General Intelligence. The average of 5 different subjects that LLMs are commonly steered away from. The leaderboard is made from roughly 60 questions overall, measuring both "willingness to answer" and "accuracy" in fact-based controversial questions.
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Willingness: A more narrow score, solely measuring the LLM's willingness to answer controversial questions.
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Unruly: Knowledge of activities that are generally frowned upon.
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Internet: Knowledge of various internet information, from professional to deviant.
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CrimeStats: Knowledge of crime statistics which are uncomfortable to talk about.
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Stories/Jokes: Ability to write offensive stories and jokes.
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PolContro: Knowledge of politically/socially controversial information.
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""")
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with gr.Column():
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with gr.Row():
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search_bar = gr.Textbox(placeholder=" 🔍 Search for a model...", show_label=False)
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with gr.Row():
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filter_columns_size = gr.CheckboxGroup(
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label="Model sizes (in billions of parameters)",
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gr.Markdown("## UGI Leaderboard", elem_classes="text-lg text-center")
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gr.Markdown("""
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UGI: Uncensored General Intelligence. The average of 5 different subjects that LLMs are commonly steered away from. The leaderboard is made from roughly 60 questions overall, measuring both "willingness to answer" and "accuracy" in fact-based controversial questions.
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+
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Willingness: A more narrow score, solely measuring the LLM's willingness to answer controversial questions.
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+
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Unruly: Knowledge of activities that are generally frowned upon.
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+
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Internet: Knowledge of various internet information, from professional to deviant.
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+
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CrimeStats: Knowledge of crime statistics which are uncomfortable to talk about.
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+
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Stories/Jokes: Ability to write offensive stories and jokes.
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+
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PolContro: Knowledge of politically/socially controversial information.
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""")
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with gr.Column():
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with gr.Row():
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search_bar = gr.Textbox(placeholder=" 🔍 Search for a model...", show_label=False, elem_id="search-bar")
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with gr.Row():
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filter_columns_size = gr.CheckboxGroup(
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label="Model sizes (in billions of parameters)",
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