Update README.md

README.md

@@ -20,22 +20,30 @@ tags:
 ## Introduction
 
 We are excited to announce the official open-source release of Ring-mini-linear-2.0!
-Building on the success of our Ling 2.0 series, this model continues to leverage a powerful hybrid architecture of linear and standard attention, perfectly balancing high performance with superior efficiency. By integrating our proven MoE design with optimizations like a 1/32 expert activation ratio and MTP layers, Ring-mini-linear achieves the performance of a massive 8 B dense model while activating only 1.4 B parameters.  This model was converted from Ling-mini-base-2.0, further trained on an additional xx B tokens.
+
+Building on the success of our Ling 2.0 series, this model continues to leverage a powerful hybrid architecture of linear and standard attention, perfectly balancing high performance with superior efficiency. By integrating our proven MoE design with optimizations like a 1/32 expert activation ratio and MTP layers, Ring-mini-linear achieves the performance of a 8 B dense model while activating only 1.4 B parameters.  This model was converted from Ling-mini-base-2.0, further trained on an additional 600 B tokens.
 When it comes to benchmarks, Ring-mini-linear-2.0 not only holds its own against standard attention models (like ring-mini-2) but also outperforms other open-source MoE and Dense models in its class on several demanding tasks. Plus, with native support for a 128k long context, it's faster and more precise than ever, especially when handling long-form inputs and outputs.
 
 <div style="display: flex; justify-content: center;">
   <div style="text-align: center;">
     <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/UsAtWWsWB9eXcMxV5iCCa.png" width="600">
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure xx:</strong> Hybrid Linear Model Architecture</p>
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 1:</strong> Hybrid Linear Model Architecture</p>
   </div>
 </div>
 
 ## Evaluation
 <!-- <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/_tjjgBEBlankfrWUY0N9i.png" width="1000"> -->
+<div style="display: flex; justify-content: center;">
+  <div style="text-align: center;">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/_tjjgBEBlankfrWUY0N9i.png" width="800">
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 3:</strong> demo </p>
+  </div>
+</div>
+
 <div style="display: flex; justify-content: center;">
   <div style="text-align: center;">
     <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/OyvwJdc0qRIJiHM1VJgwO.gif" width="800">
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure xx:</strong> demo </p>
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 3:</strong> demo </p>
   </div>
 </div>
 
@@ -48,14 +56,14 @@ The results are remarkable. In the prefill stage, Ring-mini-linear-2.0's perform
 <div style="display: flex; justify-content: center; align-items: flex-start; gap: 20px;">
   <div style="text-align: center;">
     <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/O9gHLIOCdpWvBbPC6bMM5.webp" width="500">
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure xx:</strong> Ring-mini-linear-2.0 prefill throughput</p>
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 4:</strong> Ring-mini-linear-2.0 prefill throughput</p>
   </div>
   
   <div style="text-align: center;">
     <p align="center">
       <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/AvMTStWFX-Frzv-vOzyr6.webp" width="500">
     </p>
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure xx:</strong> Ring-mini-linear-2.0 decode throughput</p>
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 5:</strong> Ring-mini-linear-2.0 decode throughput</p>
   </div>
 
 </div>