fix about

app.py

@@ -310,7 +310,7 @@ with demo:
                 )
 
         with gr.TabItem("❔ About 📖", id=4):
-            gr.Markdown(ABOUT_TEXT)
+            gr.HTML(ABOUT_TEXT)
 
     demo.load(
         change_tab,

src/assets/text_content.py

@@ -16,11 +16,13 @@ A100_TEXT = """<h3>Single-GPU Benchmark (1xA100):</h3>
 """
 
 ABOUT_TEXT = """<h3>About the benchmarks:</h3>
-- The performances benchmarks were obtained using [Optimum-Benchmark](https://github.com/huggingface/optimum-benchmark).
-- Throughput is measured in tokens per second when generating 1000 tokens with a batch size of 1.
-- Peak memory is measured in MB during the first forward pass of the model (no warmup).
-- Open LLM Score is an average evaluation score obtained from the [🤗 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
-- Open LLM Tradeoff is the euclidean distance between an LLM and the "perfect LLM" (i.e. 0 latency and 100% accuracy) translating the tradeoff between latency and accuracy.
+<ul>
+  <li>The performances benchmarks were obtained using <a href="https://github.com/huggingface/optimum-benchmark">Optimum-Benchmark</a>.</li>
+  <li>Throughput is measured in tokens per second when generating 1000 tokens with a batch size of 1.</li>
+  <li>Peak memory is measured in MB during the first forward pass of the model (no warmup).</li>
+  <li>Open LLM Score is an average evaluation score obtained from the <a href="https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard">🤗 Open LLM Leaderboard</a>.</li>
+  <li>Open LLM Tradeoff is the euclidean distance between an LLM and the "perfect LLM" (i.e. 0 latency and 100% accuracy) translating the tradeoff between latency and accuracy.</li>
+</ul>
 """
 
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results."