Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -20,11 +20,7 @@ MMLU_DATASET = "cais/mmlu"
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MMLU_PRO_DATASET = "TIGER-Lab/MMLU-Pro"
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def get_all_benchmark_options():
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Dynamically fetches all available subjects for MMLU and MMLU-Pro.
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Returns a dictionary mapping benchmark dataset IDs to their subjects,
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and a flattened list suitable for a Gradio dropdown.
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"""
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all_options = {}
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gr_dropdown_options = [] # This is for initial display only, not used for dynamic updates directly
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@@ -89,10 +85,7 @@ def load_model(model_id):
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def format_prompt(item):
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Formats a single MMLU/MMLU-Pro question item into a clear prompt for the LLM.
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The prompt is designed for the model to output a single letter answer (A, B, C, D).
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"""
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prompt = f"""{item['question']}
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A. {item['choices'][0]}
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B. {item['choices'][1]}
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@@ -647,7 +640,7 @@ with gr.Blocks(css="""
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with gr.TabItem("π Run Evaluation"):
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gr.Markdown("""
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<div class="markdown-text">
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Enter your Hugging Face Model ID, choose a benchmark (MMLU
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select a subject (or 'ALL' for a comprehensive evaluation),
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and specify the number of samples per subject.
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Ensure your Hugging Face token is set as an environment variable for private models.
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@@ -750,14 +743,14 @@ with gr.Blocks(css="""
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with gr.TabItem("π Leaderboard"):
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gr.Markdown("""
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<div class="markdown-text">
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Explore the performance of various LLMs on
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This leaderboard is updated automatically with each new evaluation.
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</div>
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""")
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# Leaderboard Type Toggle
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leaderboard_type_toggle = gr.Radio(
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["MMLU
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label="Select Benchmark for Leaderboard",
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value="MMLU", # Default to MMLU
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interactive=True,
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MMLU_PRO_DATASET = "TIGER-Lab/MMLU-Pro"
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def get_all_benchmark_options():
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all_options = {}
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gr_dropdown_options = [] # This is for initial display only, not used for dynamic updates directly
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def format_prompt(item):
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prompt = f"""{item['question']}
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A. {item['choices'][0]}
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B. {item['choices'][1]}
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with gr.TabItem("π Run Evaluation"):
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gr.Markdown("""
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<div class="markdown-text">
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Enter your Hugging Face Model ID, choose a benchmark (MMLU only for now),
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select a subject (or 'ALL' for a comprehensive evaluation),
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and specify the number of samples per subject.
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Ensure your Hugging Face token is set as an environment variable for private models.
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with gr.TabItem("π Leaderboard"):
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gr.Markdown("""
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<div class="markdown-text">
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Explore the performance of various LLMs on a chunk of MMLU called MMLU Small.
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This leaderboard is updated automatically with each new evaluation.
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</div>
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""")
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# Leaderboard Type Toggle
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leaderboard_type_toggle = gr.Radio(
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["MMLU Small"],
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label="Select Benchmark for Leaderboard",
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value="MMLU", # Default to MMLU
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interactive=True,
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