update logo

app.py

@@ -2,6 +2,7 @@
 import os
 import datetime
 import socket
+import base64
 from threading import Thread
 
 import gradio as gr
@@ -20,6 +21,7 @@ from src.display.about import (
     LLM_BENCHMARKS_DETAILS,
     FAQ_TEXT,
     TITLE,
+    ACKNOWLEDGEMENT_TEXT,
 )
 
 from src.display.css_html_js import custom_css
@@ -216,10 +218,21 @@ def load_query(request: gr.Request):
     return query
 
 
+def get_image_html(url, image_path):
+    with open(image_path, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read()).decode()
+    return f'<a href="{url}" target="_blank"><img src="data:image/jpg;base64,{encoded_string}" alt="NetMind.AI Logo" style="width:100pt;"></a>'
+
+
+# Prepare the HTML content with the image
+image_html = get_image_html("https://netmind.ai/home", "./src/display/imgs/Netmind.AI_LOGO.jpg")
+
+
 demo = gr.Blocks(css=custom_css)
 with demo:
     gr.HTML(TITLE)
     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+    gr.HTML(ACKNOWLEDGEMENT_TEXT.format(image_html=image_html))
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem("open-moe-llm-leaderboard", elem_id="llm-benchmark-tab-table", id=0):

src/display/about.py

@@ -3,7 +3,8 @@ from src.display.utils import ModelType
 TITLE = """<h1 align="center" id="space-title">OPEN-MOE-LLM-LEADERBOARD</h1>"""
 
 INTRODUCTION_TEXT = """
-The OPEN-MOE-LLM-LEADERBOARD is specifically designed to assess the performance and efficiency of various Mixture of Experts (MoE) Large Language Models (LLMs). This initiative, driven by the open-source community, aims to comprehensively evaluate these advanced MoE LLMs. We extend our gratitude to the Huggingface for the GPU community grant that supported the initial debugging process, and to [NetMind.AI](https://netmind.ai/home) for their generous GPU donation, which ensures the continuous operation of the Leaderboard.
+The OPEN-MOE-LLM-LEADERBOARD is specifically designed to assess the performance and efficiency of various Mixture of Experts (MoE) Large Language Models (LLMs). 
+This initiative, driven by the open-source community, aims to comprehensively evaluate these advanced MoE LLMs. 
 
 The OPEN-MOE-LLM-LEADERBOARD includes generation and multiple choice tasks to measure the performance and efficiency of MOE LLMs.
 
@@ -20,6 +21,15 @@ Columns and Metrics:
 - Precision: The precison of used model.
 
 """
+
+ACKNOWLEDGEMENT_TEXT = """
+<div>
+    <h4>Acknowledgements</h4>
+    {image_html}
+    <p>We express our sincere gratitude to <a href="https://netmind.ai/home">NetMind.AI</a> for their generous donation of GPUs, which plays a crucial role in ensuring the continuous operation of our Leaderboard.</p>
+</div>
+"""
+
 LLM_BENCHMARKS_TEXT = f"""
 
 """