diff --git a/_sass/color_schemes/onnxruntime.scss b/_sass/color_schemes/onnxruntime.scss
index 4e0cc934e1881..a5e5cd7de7a14 100644
--- a/_sass/color_schemes/onnxruntime.scss
+++ b/_sass/color_schemes/onnxruntime.scss
@@ -2,12 +2,83 @@ $link-color: #226aca;
 $btn-primary-color: #226aca;
 
 // Code is too light in default theme //
-.highlight .n {
-	color: #555 !important;
-}
-.highlight .nn {
-	color: #555 !important;
-}
-.highlight .c1 {
-	color: #188616 !important;
-}
+// .highlight .n {
+// 	color: #555 !important;
+// }
+// .highlight .nn {
+// 	color: #555 !important;
+// }
+// .highlight .c1 {
+// 	color: #188616 !important;
+// }
+
+.highlight .hll { background-color: #49483e; }
+.highlight { background: #272822; color: #f8f8f2; }
+.highlight .c { color: #949076; }
+.highlight .err { background-color: #1e0010; color: #eb0083; }
+.highlight .k { color: #66d9ef; }
+.highlight .l { color: #ae81ff; }
+.highlight .n { color: #f8f8f2; }
+.highlight .o { color: #f94e8a; }
+.highlight .p { color: #f8f8f2; }
+.highlight .ch { color: #949076; }
+.highlight .cm { color: #949076; }
+.highlight .cp { color: #949076; }
+.highlight .cpf { color: #949076; }
+.highlight .c1 { color: #949076; }
+.highlight .cs { color: #949076; }
+.highlight .gd { color: #f94e8a; }
+.highlight .ge { font-style: italic; }
+.highlight .gi { color: #a6e22e; }
+.highlight .gs { font-weight: bold; }
+.highlight .gu { color: #949076; }
+.highlight .kc { color: #66d9ef; }
+.highlight .kd { color: #66d9ef; }
+.highlight .kn { color: #f94e8a; }
+.highlight .kp { color: #66d9ef; }
+.highlight .kr { color: #66d9ef; }
+.highlight .kt { color: #66d9ef; }
+.highlight .ld { color: #e6db74; }
+.highlight .m { color: #ae81ff; }
+.highlight .s { color: #e6db74; }
+.highlight .na { color: #a6e22e; }
+.highlight .nb { color: #f8f8f2; }
+.highlight .nc { color: #a6e22e; }
+.highlight .no { color: #66d9ef; }
+.highlight .nd { color: #a6e22e; }
+.highlight .ni { color: #f8f8f2; }
+.highlight .ne { color: #a6e22e; }
+.highlight .nf { color: #a6e22e; }
+.highlight .nl { color: #f8f8f2; }
+.highlight .nn { color: #f8f8f2; }
+.highlight .nx { color: #a6e22e; }
+.highlight .py { color: #f8f8f2; }
+.highlight .nt { color: #f94e8a; }
+.highlight .nv { color: #f8f8f2; }
+.highlight .ow { color: #f94e8a; }
+.highlight .w { color: #f8f8f2; }
+.highlight .mb { color: #ae81ff; }
+.highlight .mf { color: #ae81ff; }
+.highlight .mh { color: #ae81ff; }
+.highlight .mi { color: #ae81ff; }
+.highlight .mo { color: #ae81ff; }
+.highlight .sa { color: #e6db74; }
+.highlight .sb { color: #e6db74; }
+.highlight .sc { color: #e6db74; }
+.highlight .dl { color: #e6db74; }
+.highlight .sd { color: #e6db74; }
+.highlight .s2 { color: #e6db74; }
+.highlight .se { color: #ae81ff; }
+.highlight .sh { color: #e6db74; }
+.highlight .si { color: #e6db74; }
+.highlight .sx { color: #e6db74; }
+.highlight .sr { color: #e6db74; }
+.highlight .s1 { color: #e6db74; }
+.highlight .ss { color: #e6db74; }
+.highlight .bp { color: #f8f8f2; }
+.highlight .fm { color: #a6e22e; }
+.highlight .vc { color: #f8f8f2; }
+.highlight .vg { color: #f8f8f2; }
+.highlight .vi { color: #f8f8f2; }
+.highlight .vm { color: #f8f8f2; }
+.highlight .il { color: #ae81ff; }
\ No newline at end of file
diff --git a/docs/tutorials/on-device-training/ios-app.md b/docs/tutorials/on-device-training/ios-app.md
index 76f485a2e2648..fff1347923ef0 100644
--- a/docs/tutorials/on-device-training/ios-app.md
+++ b/docs/tutorials/on-device-training/ios-app.md
@@ -15,7 +15,7 @@ In this tutorial, we will build a simple speaker identification app that learns
 Here is what the application will look like:
 
 
-<img src="../../../images/iOS_speaker_identification_app.png"  width="30%" height="30%">
+<img src="../../../images/iOS_speaker_identification_app.png" alt="application demo, with buttons for voice, train, and infer." width="30%" height="30%">
 
 ## Introduction
 We will guide you through the process of building an iOS application that can train a simple audio classification model using on-device training techniques. The tutorial showcases the `transfer learning` technique where knowledge gained from training a model on one task is leveraged to improve the performance of a model on a different but related task. Instead of starting the learning process from scratch, transfer learning allows us to transfer the knowledge or features learned by a pre-trained model to a new task.
@@ -30,28 +30,22 @@ In the tutorial, we will:
 
 
 ## Contents
-- [Introduction](#introduction)
-- [Prerequisites](#prerequisites)
-- [Generating the training artifacts](#generating-the-training-artifacts)
-    - [Export the model to ONNX](#export-the-model-to-onnx)
-    - [Define the trainable and non trainable parameters](#define-the-trainable-and-non-trainable-parameters)
-    - [Generate the training artifacts](#generate-the-training-artifacts)
-
-- [Building the iOS application](#building-the-ios-application)
+- [Building an iOS Application](#building-an-ios-application)
+  - [Introduction](#introduction)
+  - [Contents](#contents)
+  - [Prerequisites](#prerequisites)
+  - [Generating the training artifacts](#generating-the-training-artifacts)
+  - [Building the iOS application](#building-the-ios-application)
     - [Xcode Setup](#xcode-setup)
     - [Application Overview](#application-overview)
     - [Training the model](#training-the-model)
-        - [Loading the training artifacts and initializing training session](#loading-the-training-artifacts-and-initializing-training-session)
-        - [Training the model](#training-the-model-1)
-        - [Exporting the trained model](#exporting-the-trained-model)
-
     - [Inference with the trained model](#inference-with-the-trained-model)
     - [Recording Audio](#recording-audio)
     - [Train View](#train-view)
     - [Infer View](#infer-view)
     - [ContentView](#contentview)
-- [Running the iOS application](#running-the-ios-application)
-- [Conclusion](#conclusion)
+  - [Running the iOS application](#running-the-ios-application)
+  - [Conclusion](#conclusion)
 
 
 ## Prerequisites