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dev/user-guide/getting-started/how_to_use/index.html

@@ -2251,7 +2251,7 @@ <h2 id="run-configurations">Run configurations</h2>
 <h3 id="config-files">Config files</h3>
 <p>The setup for an <em>eva</em> run is provided in a <code>.yaml</code> config file which is defined with the <code>--config</code> flag.</p>
 <p>A config file specifies the setup for the <em>trainer</em> (including callback for the model backbone), the <em>model</em> (setup of the trainable decoder) and <em>data</em> module. </p>
-<p>The config files for the datasets and models that <em>eva</em> supports out of the box, you can find on <a href="https://github.com/kaiko-ai/eva/releases/tag/0.0.1">GitHub</a> (scroll to the bottom of the page). We recommend that you inspect some of them to get a better understanding of their structure and content.</p>
+<p>The config files for the datasets and models that <em>eva</em> supports out of the box, you can find on <a href="https://github.com/kaiko-ai/eva/tree/0.0.2">GitHub</a>. We recommend that you inspect some of them to get a better understanding of their structure and content.</p>
 <h3 id="environment-variables">Environment variables</h3>
 <p>To customize runs, without the need of creating custom config-files, you can overwrite the config-parameters listed below by setting them as environment variables.</p>
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dev/user-guide/tutorials/offline_vs_online/index.html

@@ -2231,7 +2231,7 @@
 <h1 id="offline-vs-online-evaluations"><em>Offline</em> vs. <em>online</em> evaluations</h1>
 <p>In this tutorial we run <em>eva</em> with the three subcommands <code>predict</code>, <code>fit</code> and <code>predict_fit</code>, and take a look at the difference between <em>offline</em> and <em>online</em> workflows.</p>
 <h3 id="before-you-start">Before you start</h3>
-<p>If you haven't downloaded the config files yet, please download them from <a href="https://github.com/kaiko-ai/eva/releases/tag/0.0.1">GitHub</a> (scroll to the bottom of the page).</p>
+<p>If you haven't downloaded the config files yet, please download them from <a href="https://github.com/kaiko-ai/eva/tree/0.0.2">GitHub</a>.</p>
 <p>For this tutorial we use the <a href="../../../datasets/bach/">BACH</a> classification task which is available on <a href="https://zenodo.org/records/3632035">Zenodo</a> and is distributed under <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode"><em>Attribution-NonCommercial-ShareAlike 4.0 International</em></a> license.</p>
 <p>To let <em>eva</em> automatically handle the dataset download, you can open <code>configs/vision/dino_vit/offline/bach.yaml</code> and set <code>download: true</code>. Before doing so, please make sure that your use case is compliant with the dataset license.</p>
 <h2 id="offline-evaluations"><em>Offline</em> evaluations</h2>