added citation

app.py

@@ -3,7 +3,7 @@ import gradio as gr
 import pandas as pd
 from apscheduler.schedulers.background import BackgroundScheduler
 
-from src.assets.text_content import TITLE, INTRODUCTION_TEXT
+from src.assets.text_content import TITLE, INTRODUCTION_TEXT, CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT
 from src.assets.css_html_js import custom_css, get_window_url_params
 from src.utils import restart_space, load_dataset_repo, make_clickable_model
 
@@ -61,8 +61,8 @@ with demo:
     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🖥️ 4xA100-80GB Benchmark 🏋️", elem_id="a100-benchmark", id=0):
-            dataframe_text = "<h4>Batch Size: 1 ; Generated Tokens: 100</h4>"
+        with gr.TabItem("📊 A100-80GB Benchmark 🏋️", elem_id="a100-benchmark", id=0):
+            dataframe_text = "<h4>Specification:\nSingle and Multi-GPU Setup\nBatch Size: 1\nGenerated Tokens: 100</h4>"
 
             gr.HTML(dataframe_text)
             benchmark_df = get_benchmark_df()
@@ -70,9 +70,17 @@ with demo:
                 value=benchmark_df,
                 datatype=COLUMNS_DATATYPES,
                 headers=NEW_COLUMNS,
-                elem_id="pytorch-a100-benchmark",
+                elem_id="pytorch-A100-benchmark",
             )
 
+    with gr.Row():
+        with gr.Column():
+            with gr.Accordion("📙 Citation", open=False):
+                citation_button = gr.Textbox(
+                    value=CITATION_BUTTON_TEXT,
+                    label=CITATION_BUTTON_LABEL,
+                    elem_id="citation-button",
+                ).style(show_copy_button=True)
 
 # Restart space every hour
 scheduler = BackgroundScheduler()

src/assets/text_content.py

@@ -1,8 +1,21 @@
 TITLE = """<h1 align="center" id="space-title">🤗 Open LLM-Perf Leaderboard 🏋️</h1>"""
 
 INTRODUCTION_TEXT = f"""
-The 🤗 Open LLM-Perf Leaderboard 🏋️ aims to benchmark the performance (latency & throughput) of Large Language Models (LLMs) on different hardwares and backends using [Optimum-Benchmark](https://github.com/huggingface/optimum-benchmark).
+The 🤗 Open LLM-Perf Leaderboard 🏋️ aims to benchmark the performance (latency & throughput) of Large Language Models (LLMs) on different hardwares and backends using [Optimum-Benchmark](https://github.com/huggingface/optimum-benchmark) and [Optimum](https://github.com/huggingface/optimum) flavors.
 Anyone from the community can submit a model or a hardware+backend configuration for automated benchmarking:
-- Model submissions should be made in the [🤗 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) and will be added to the 🤗 Open LLM-Perf Leaderboard 🏋️ if they're accepted.
-- Hardware+Backend submissions should be made in the 🤗 Open LLM-Perf Leaderboard 🏋️ [community discussions](https://huggingface.co/spaces/optimum/llm-perf-leaderboard/discussions).
+- Model submissions should be made in the [🤗 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) and will be added to the 🤗 Open LLM-Perf Leaderboard 🏋️ once they're publicly available.
+- Hardware+Backend submissions should be made in the 🤗 Open LLM-Perf Leaderboard 🏋️ [community discussions](https://huggingface.co/spaces/optimum/llm-perf-leaderboard/discussions); An automated process will be set up soon to allow for direct submissions.
 """
+
+CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
+CITATION_BUTTON_TEXT = """@misc{open-llm-perf-leaderboard,
+  author = {Ilyas Moutawwakil},
+  title = {Open LLM-Perf Leaderboard},
+  year = {2023},
+  publisher = {Hugging Face},
+  howpublished = "\url{https://huggingface.co/spaces/optimum/open-llm-perf-leaderboard}",
+@software{optimum-benchmark,
+  author       = {Ilyas Moutawwakil},
+  title        = {A framework for benchmarking the performance of Transformers models},
+}
+"""