typo, urls and congrats on the strong model!

README.md

@@ -52,7 +52,7 @@ Key improvements over Gemma-2-2B baseline:
 - ARC-DE: +41% (32.3% vs 22.9%)
 - Average zero-shot: +40% (35.8% vs 25.5%)
 
-→ BübleLM-2B onsistently outperforms both the base Gemma-2-2B and other German models like LLaMmlein-1B across most tasks.
+→ BübleLM-2B consistently outperforms both the base Gemma-2-2B and other German models like LLäMmlein-1B across most tasks.
 
 <table class="model-comparison">
   <thead>
@@ -75,7 +75,7 @@ Key improvements over Gemma-2-2B baseline:
   </thead>
   <tbody>
     <tr>
-      <td>Gemma-2-2B</td>
+      <td><a href="https://huggingface.co/google/gemma-2-2b" target="_blank">Gemma-2-2B</a></td>
       <td align="center">22.9</td>
       <td align="center">23.1</td>
       <td align="center">28.0</td>
@@ -84,7 +84,7 @@ Key improvements over Gemma-2-2B baseline:
       <td align="center">25.5</td>
     </tr>
     <tr>
-      <td>LLaMmlein-120M</td>
+      <td><a href="https://huggingface.co/LSX-UniWue/LLaMmlein_120M" target="_blank">LLäMmlein-120M</a></td>
       <td align="center">24.7 ↑+8%</td>
       <td align="center">-</td>
       <td align="center">32.0 ↑+14%</td>
@@ -93,7 +93,7 @@ Key improvements over Gemma-2-2B baseline:
       <td align="center">27.2 ↑+7%</td>
     </tr>
     <tr>
-      <td>LLaMmlein-1B</td>
+      <td><a href="https://huggingface.co/LSX-UniWue/LLaMmlein_1B" target="_blank">LLäMmlein-1B</a></td>
       <td align="center">30.0 ↑+31%</td>
       <td align="center">-</td>
       <td align="center"><strong>48.5</strong> ↑+73%</td>
@@ -102,7 +102,7 @@ Key improvements over Gemma-2-2B baseline:
       <td align="center">34.0 ↑+33%</td>
     </tr>
     <tr>
-      <td>Sauerkraut-Gemma-2B</td>
+      <td><a href="https://huggingface.co/VAGOsolutions/SauerkrautLM-Gemma-2b" target="_blank">Sauerkraut-Gemma-2B</a></td>
       <td align="center">28.0 ↑+22%</td>
       <td align="center">34.6 ↑+50%</td>
       <td align="center">37.2 ↑+33%</td>