Editorial update

Author: madeline-underwood

content/learning-paths/smartphones-and-mobile/Build-Llama3-Chat-Android-App-Using-Executorch-And-XNNPACK/1-dev-env-setup.md

@@ -16,23 +16,23 @@ The first step is to prepare a development environment with the required softwar
 - Android NDK version 28.0.12433566.
 - Java 17 JDK.
 - Git.
-- Python 3.10 or later (these instructions have been tested with 3.10 and 3.12)
+- Python 3.10 or later (these instructions have been tested with 3.10 and 3.12).
 
-The instructions assume macOS with Apple Silicon, an x86 Debian, or Ubuntu Linux machine with at least 16GB of RAM.
+The instructions assume macOS with Apple Silicon, an x86 Debian, or an Ubuntu Linux machine, with at least 16GB of RAM.
 
 ## Install Android Studio and Android NDK
 
 Follow these steps to install and configure Android Studio:
 
 1. Download and install the latest version of [Android Studio](https://developer.android.com/studio/).
 
-2. Start Android Studio and open the `Settings` dialog.
+2. Start Android Studio and open the **Settings** dialog.
 
-3. Navigate to `Languages & Frameworks -> Android SDK`.
+3. Navigate to **Languages & Frameworks**, then **Android SDK**.
 
-4. In the `SDK Platforms` tab, check `Android 14.0 ("UpsideDownCake")`.
+4. In the **SDK Platforms** tab, check **Android 14.0 ("UpsideDownCake")**.
 
-Next, install the specific version of the Android NDK that you need by first installing the Android command line tools:
+Next, install the specific version of the Android NDK that you require by first installing the Android command line tools:
 
 Linux:
 
@@ -52,7 +52,7 @@ Unzip the Android command line tools:
 unzip commandlinetools.zip -d android-sdk
 ```
 
-Install the NDK in the directory that Android Studio installed the SDK. This is generally `~/Library/Android/sdk` by default. Set the requirement environment variables:
+Install the NDK in the same directory that Android Studio installed the SDK. This is generally `~/Library/Android/sdk` by default. Set the requirement environment variables:
 
 ```
 export ANDROID_HOME="$(realpath ~/Library/Android/sdk)"

content/learning-paths/smartphones-and-mobile/Build-Llama3-Chat-Android-App-Using-Executorch-And-XNNPACK/2-executorch-setup.md

@@ -19,13 +19,13 @@ python3.10 -m venv executorch
 source executorch/bin/activate
 ```
 
-The prompt of your terminal has (executorch) as a prefix to indicate the virtual environment is active.
+The prompt of your terminal has `executorch` as a prefix to indicate the virtual environment is active.
 
 ### Option 2: Create a Conda virtual environment
 
 Install Miniconda on your development machine by following the [Installing conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html) instructions.
 
-Once `conda` is installed create the environment:
+Once `conda` is installed, create the environment:
 
 ```bash
 conda create -yn executorch python=3.10.0
@@ -46,4 +46,4 @@ git submodule update --init
 ./examples/models/llama/install_requirements.sh
 ```
 
-When these scripts finish successfully, ExecuTorch is all set up. That means it's time to dive into the world of Llama models!
+When these scripts finish successfully, ExecuTorch is set up. That means it's time to dive into the world of Llama models!

content/learning-paths/smartphones-and-mobile/Build-Llama3-Chat-Android-App-Using-Executorch-And-XNNPACK/3-Understanding-LLaMA-models.md

@@ -43,7 +43,7 @@ The results are for two different groupsizes, with max_seq_len 2048, and 1000 sa
 |Llama 2 7B | 9.2 | 10.2 | 10.7
 |Llama 3 8B | 7.9 | 9.4 | 9.7
 
-Note that groupsize less than 128 was not enabled, since such a model was still too large. This is because current efforts have focused on enabling FP32, and support for FP16 is under way.
+Note that groupsize less than 128 was not enabled in this example, since the model was still too large. This is because current efforts have focused on enabling FP32, and support for FP16 is under way.
 
 What this implies for model size is:
 

content/learning-paths/smartphones-and-mobile/Build-Llama3-Chat-Android-App-Using-Executorch-And-XNNPACK/4-Prepare-LLaMA-models.md

@@ -8,7 +8,7 @@ layout: learningpathall
 
 ## Download and export the Llama 3.2 1B model
 
-To get started with Llama 3, you obtain the pre-trained parameters by visiting [Meta's Llama Downloads](https://llama.meta.com/llama-downloads/) page. Request the access by filling out your details and read through and accept the Responsible Use Guide. This grants you a license and a download link which is valid for 24 hours. The Llama 3.2 1B model is used for this part, but the same instructions apply for other options as well with minimal modification.
+To get started with Llama 3, you can obtain the pre-trained parameters by visiting [Meta's Llama Downloads](https://llama.meta.com/llama-downloads/) page. Request access by filling out your details, and read through and accept the Responsible Use Guide. This grants you a license and a download link which is valid for 24 hours. The Llama 3.2 1B model is used for this exercise, but the same instructions apply to other options as well with minimal modification.
 
 Install the `llama-stack` package from `pip`.
 ```bash