put everything in about tab

app.py

@@ -8,7 +8,7 @@ from src.assets.css_html_js import custom_css, custom_js
 from src.assets.text_content import (
     TITLE,
     INTRODUCTION_TEXT,
-    A100_TEXT,
+    ABOUT_TEXT,
     CITATION_BUTTON_LABEL,
     CITATION_BUTTON_TEXT,
 )
@@ -224,8 +224,6 @@ with demo:
     # leaderboard tabs
     with gr.Tabs(elem_classes="A100-tabs") as A100_tabs:
         with gr.TabItem("🖥️ A100-80GB Leaderboar Table 🏆", id=0):
-            gr.HTML(A100_TEXT)
-
             # Original leaderboard table
             A100_leaderboard = gr.components.Dataframe(
                 value=A100_table,
@@ -235,8 +233,6 @@ with demo:
             )
 
         with gr.TabItem("🖥️ A100-80GB Interactive Plot 📊", id=2):
-            gr.HTML(A100_TEXT)
-
             # Original leaderboard plot
             A100_plotly = gr.components.Plot(
                 value=A100_plot,
@@ -304,6 +300,9 @@ with demo:
                     elem_id="filter-button",
                 )
 
+        with gr.TabItem("📖 About ❔", id=4):
+            gr.HTML(ABOUT_TEXT)
+
     demo.load(
         change_tab,
         A100_tabs,
@@ -344,3 +343,4 @@ scheduler.start()
 
 # Launch demo
 demo.queue(concurrency_count=40).launch()
+demo.queue(concurrency_count=40).launch()

src/assets/text_content.py

@@ -8,8 +8,9 @@ Anyone from the community can request a model or a hardware/backend/optimization
 - Hardware/Backend/Optimization performance requests should be made in the [community discussions](https://huggingface.co/spaces/optimum/llm-perf-leaderboard/discussions) to assess their relevance and feasibility.
 """
 
-A100_TEXT = """<h3>Single-GPU Benchmark (1xA100):</h3>
+ABOUT_TEXT = """<h3>About the 🤗 Open LLM-Perf Leaderboard 🏋️</h3>
 <ul>
+    <li>To avoid communication-dependent results, only one GPU is used.</li>
     <li>LLMs are evaluated on a singleton batch and genrating a thousand tokens.</li>
     <li>Peak memory is measured in MB during the first forward pass of the LLM (no warmup).</li>
     <li>Each pair of (Model Type, Weight Class) is represented by the best scored model. This LLM is the one used for all the hardware/backend/optimization experiments.</li>