Editorial

Author: madeline-underwood

content/learning-paths/smartphones-and-mobile/Build-Llama3-Chat-Android-App-Using-Executorch-And-XNNPACK/5-Run-Benchmark-on-Android.md

@@ -12,7 +12,7 @@ Cross-compile Llama runner to run on Android using the steps below.
 
 ### 1. Set Android NDK
 
-Set the environment variable to point to the Android NDK.
+Set the environment variable to point to the Android NDK:
 
 ``` bash
 export ANDROID_NDK=$ANDROID_HOME/ndk/28.0.12433566/
@@ -122,4 +122,4 @@ I 00:00:09.624432 executorch:stats.h:154] 	Time to first generated token:	0.2020
 I 00:00:09.624434 executorch:stats.h:161] 	Sampling time over 127 tokens:	0.110000 (seconds)
 ```
 
-You have successfully run the llama 3.1 1B model on your Android smartphone with ExecuTorch using KleidiAI kernels.
+You have successfully run the Llama 3.1 1B model on your Android smartphone with ExecuTorch using KleidiAI kernels.

content/learning-paths/smartphones-and-mobile/Build-Llama3-Chat-Android-App-Using-Executorch-And-XNNPACK/6-Build-Android-Chat-App.md

@@ -21,13 +21,14 @@ You can use the Android demo application included in ExecuTorch repository [Llam
     ```
 
 {{% notice Note %}}
-<path_to_android_ndk> is the root for the NDK, which is usually under ~/Library/Android/sdk/ndk/XX.Y.ZZZZZ for macOS, and contains NOTICE and README.md. Make sure you can confirm <path_to_android_ndk>/build/cmake/android.toolchain.cmake is available for CMake to cross-compile.
+<path_to_android_ndk> is the root for the NDK, which is usually under ~/Library/Android/sdk/ndk/XX.Y.ZZZZZ for macOS, and contains NOTICE and README.md. 
+Make sure you can confirm <path_to_android_ndk>/build/cmake/android.toolchain.cmake is available for CMake to cross-compile.
 {{% /notice %}}
 
-3. (Optional) If you need to use tiktoken as the tokenizer (for LLaMA 3), set `EXECUTORCH_USE_TIKTOKEN=ON` and CMake uses it as the tokenizer. If you run other models like LLaMA 2, skip this step.
+3. (Optional) If you need to use tiktoken as the tokenizer (for LLama 3), set `EXECUTORCH_USE_TIKTOKEN=ON`, and CMake uses it as the tokenizer. If you run other models such as LLama 2, skip this step.
 
     ``` bash
-    export EXECUTORCH_USE_TIKTOKEN=ON # Only for LLaMA3
+    export EXECUTORCH_USE_TIKTOKEN=ON # Only for LLama3
     ```
 
 4. Run the following commands to set up the required JNI library:
@@ -83,7 +84,7 @@ If the files are not on the device, use the device explorer to copy them.
 
 This is the recommended option.
 
-1. Open Android Studio and select “Open an existing Android Studio project” and navigate to open `examples/demo-apps/android/LlamaDemo`.
+1. Open Android Studio and select **Open an existing Android Studio project** and navigate to open `examples/demo-apps/android/LlamaDemo`.
 
 2. Run the app (^R). This builds and launches the app on the phone.
 

content/learning-paths/smartphones-and-mobile/Build-Llama3-Chat-Android-App-Using-Executorch-And-XNNPACK/_review.md

@@ -20,7 +20,7 @@ review:
             - Llama is a family of large language models that uses publicly-available data for training.
         correct_answer: 3                   
         explanation: >
-            LLaMA is a state-of-the-art foundational large language model designed to enable researchers to advance their work in this subfield of AI.
+            LLama is a state-of-the-art foundational large language model designed to enable researchers to advance their work in this subfield of AI.
                
     - questions:
         question: >