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Update app.py

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Files changed (1) hide show
  1. app.py +3 -4
app.py CHANGED
@@ -179,7 +179,7 @@ with gr.Blocks(css=css_tech_theme) as demo:
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  gr.Markdown("""
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  <div class="center-content">
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  <h1>πŸ† Mobile-MMLU Benchmark Competition</h1>
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- <h3>🌟 Welcome to the Competition Overview</h3>
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  <img src="https://via.placeholder.com/200" alt="Competition Logo">
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  <p>
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  Welcome to the Mobile-MMLU Benchmark Competition. Here you can submit your predictions,
@@ -192,13 +192,12 @@ with gr.Blocks(css=css_tech_theme) as demo:
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  with gr.Tabs(elem_id="tabs"):
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  with gr.TabItem("πŸ“– Overview"):
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  gr.Markdown("""
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- ## Overview
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  Welcome to the **Mobile-MMLU Benchmark Competition**! Evaluate mobile-compatible Large Language Models (LLMs) on **16,186 scenario-based and factual questions** across **80 fields**.
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  ---
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- ### What is Mobile-MMLU?
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  Mobile-MMLU is a benchmark designed to test the capabilities of LLMs optimized for mobile use. Contribute to advancing mobile AI systems by competing to achieve the highest accuracy.
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  ---
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- ### How It Works
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  1. **Download the Dataset**
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  Access the dataset and instructions on our [GitHub page](https://github.com/your-github-repo).
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  2. **Generate Predictions**
 
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  gr.Markdown("""
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  <div class="center-content">
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  <h1>πŸ† Mobile-MMLU Benchmark Competition</h1>
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+ <h3>🌟 Welcome to the Competition</h3>
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  <img src="https://via.placeholder.com/200" alt="Competition Logo">
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  <p>
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  Welcome to the Mobile-MMLU Benchmark Competition. Here you can submit your predictions,
 
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  with gr.Tabs(elem_id="tabs"):
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  with gr.TabItem("πŸ“– Overview"):
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  gr.Markdown("""
 
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  Welcome to the **Mobile-MMLU Benchmark Competition**! Evaluate mobile-compatible Large Language Models (LLMs) on **16,186 scenario-based and factual questions** across **80 fields**.
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  ---
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+ ## What is Mobile-MMLU?
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  Mobile-MMLU is a benchmark designed to test the capabilities of LLMs optimized for mobile use. Contribute to advancing mobile AI systems by competing to achieve the highest accuracy.
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  ---
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+ ## How It Works
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  1. **Download the Dataset**
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  Access the dataset and instructions on our [GitHub page](https://github.com/your-github-repo).
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  2. **Generate Predictions**