add links to github code

Author: lsarra

_layouts/post.html

@@ -19,12 +19,17 @@ <h4 class="category">{{ page.date | date: "%b" }} {{ page.date | date: "%d" }}</
       </div>
     </header>
 
+    <p>
     <!-- Buttons -->
     {% if page.link != nil %}
-      <p>
         <a href={{ page.link }} class="button-post">Paper</a>
-      </p>
     {% endif %}
+
+    {% if page.link != nil %}
+      <a href={{ page.github_link }} class="button-post">Code</a>
+    {% endif %}
+    </p>
+
     <!-- Article Content -->
     <div class="article-content post-article-content">
 

_posts/2023-10-09-astroclip.md

@@ -10,6 +10,7 @@ blurb: We present a self-supervised learning strategy that bridges diverse obser
 shortblurb: We present a self-supervised learning strategy that bridges diverse observational modalities in astrophysics. By aligning cross-modal representations of galaxies in a shared space, we are able to perform cross-modal look-up and competitive zero-shot predictions on downstream tasks.  
 splashimage: /images/blog/astroclip.jpeg
 link: https://arxiv.org/abs/2310.03024
+github_link: https://github.com/PolymathicAI/AstroCLIP
 permalink: /blog/astroclip/
 ---
 

_posts/2023-10-09-mpp.md

@@ -10,6 +10,7 @@ blurb: We introduce Multiple Physics Pretraining, a new approach for developing
 shortblurb: Introducing Multiple Physics Pretraining, a new approach for developing large pretrained physical surrogate models. Our approach uses a built-in normalization and embedding scheme to enable learning multiple physical dynamics with a single model.
 splashimage: /images/blog/waves.jpg
 link: https://arxiv.org/abs/2310.02994
+github_link: https://github.com/PolymathicAI/multiple_physics_pretraining
 permalink: /blog/mpp/
 ---
 

_posts/2023-10-09-xval.md

@@ -10,6 +10,7 @@ blurb: We introduce xVal, a new number encoding scheme for LLMs. Using xVal with
 shortblurb: We introduce xVal, a new number encoding scheme for LLMs. Using xVal with a modified number inference method makes LLMs continuous function approximators. This makes them have a better inductive bias for data analysis in scientific domains.
 splashimage: /images/blog/xval-splash.jpg
 link: https://arxiv.org/abs/2310.02989
+github_link: https://github.com/PolymathicAI/xVal
 permalink: /blog/xval/
 ---