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README.md

@@ -28,11 +28,17 @@ PARD is a high-performance speculative decoding method that also enables low-cos
 
 - **High Performance**: When integrated into an optimized inference framework called Transformers+ PARD delivers up to a 4.08× speedup, with LLaMA3.1 8B reaches a state-of-the-art 311.5 tokens per second. When integrated into vLLM, PARD delivers up to 3.06× speedup, outperforming other speculative decoding methods in vLLM by 1.51×.
 
+
 <p align="center">
-  <picture><img src="https://cdn-uploads.huggingface.co/production/uploads/630cb01cc169245d78fe76b6/Dh-7wE-l0YAfU9lXWssKf.png" width="90%"></picture>
-  <br><div align="center" width="90%"><em>AR and AR+ represent baseline auto-regressive generation using Transformers and Transformers+, respectively. VSD denotes vanilla speculative decoding. PARD refers to the proposed method in this work.</em></div><br>
+  <figure style="display: inline-block; text-align: center;">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/630cb01cc169245d78fe76b6/Dh-7wE-l0YAfU9lXWssKf.png" width="90%">
+    <figcaption style="font-style: italic; margin-top: 2px;">
+      AR and AR+ represent baseline auto-regressive generation using Transformers and Transformers+, respectively. VSD denotes vanilla speculative decoding. PARD refers to the proposed method in this work.
+    </figcaption>
+  </figure>
 </p>
 
+
 ## Model Weights
 
 | Model Series | Model Name                            | Download      |
@@ -56,4 +62,3 @@ Please visit [PARD](https://github.com/AMD-AIG-AIMA/PARD) repo for more informat
   year={2025}
 }
 ```
-