docs: apply same changes to 0.8 version

Author: msluszniak

docs/versioned_docs/version-0.8.x/01-fundamentals/01-getting-started.md

@@ -26,7 +26,7 @@ import TabItem from '@theme/TabItem';
 
 ## React Native ExecuTorch
 
-React Native ExecuTorch is our way of bringing ExecuTorch into the React Native world. Our API is built to be simple, declarative, and efficient. Plus, we’ll provide a set of pre-exported models for common use cases, so you won’t have to worry about handling exports yourself. With just a few lines of JavaScript, you’ll be able to run AI models (even LLMs 👀) right on your device—keeping user data private and saving on cloud costs.
+React Native ExecuTorch is our way of bringing ExecuTorch into the React Native world. Our API is built to be simple, declarative, and efficient. Additionally, we provide a set of pre-exported models for common use cases, so you don’t have to worry about handling exports yourself. With just a few lines of JavaScript, you can run AI models (even LLMs 👀) right on your device—keeping user data private and saving on cloud costs.
 
 ## Compatibility
 
@@ -122,11 +122,11 @@ yarn <ios | android> -d
 
 Adding new functionality to the library follows a consistent three-step integration pipeline:
 
-1. **Model Serialization:** We export PyTorch models for specific tasks (e.g., object detection) into the \*.pte format, which is optimized for the ExecuTorch runtime.
+1. **Model Serialization:** Export PyTorch model for a specific task (e.g. object detection) into the `*.pte` format, which is optimized for the ExecuTorch runtime.
 
-2. **Native Implementation:** We develop a C++ execution layer that interfaces with the ExecuTorch runtime to handle inference. This layer also manages model-dependent logic, such as data pre-processing and post-processing.
+2. **Native Implementation:** Develop a C++ execution layer that interfaces with the ExecuTorch runtime to handle inference. This layer also manages model-dependent logic, such as data pre-processing and post-processing.
 
-3. **TS Bindings:** Finally, we implement a TypeScript API that bridges the JavaScript environment to the native C++ logic, providing a clean, typed interface for the end user."
+3. **TS Bindings:** Finally, implement a TypeScript API that bridges the JavaScript environment to the native C++ logic, providing a clean, typed interface for the end user.
 
 ## Good reads
 

docs/versioned_docs/version-0.8.x/01-fundamentals/02-loading-models.md

@@ -46,15 +46,15 @@ initExecutorch({
 
 ## Loading
 
-**1. Load from React Native assets folder (For Files < 512MB)**
+### Load from React Native assets folder (for files < 512MB)
 
 ```typescript
 useExecutorchModule({
   modelSource: require('../assets/llama3_2.pte'),
 });
 ```
 
-**2. Load from remote URL:**
+### Load from remote URL
 
 For files larger than 512MB or when you want to keep size of the app smaller, you can load the model from a remote URL (e.g. HuggingFace).
 
@@ -64,7 +64,7 @@ useExecutorchModule({
 });
 ```
 
-**3. Load from local file system:**
+### Load from local file system
 
 If you prefer to delegate the process of obtaining and loading model and tokenizer files to the user, you can use the following method:
 

docs/versioned_docs/version-0.8.x/02-benchmarks/inference-time.md

@@ -91,7 +91,9 @@ The values below represent the averages across all runs for the benchmark image.
 
 ## Vertical OCR
 
-Notice that the recognizer models, as well as detector's `forward_320` method, were executed between 4 and 21 times during a single recognition.
+:::note
+Recognizer models, as well as detector's `forward_320` method, were executed between 4 and 21 times during a single recognition.
+:::
 The values below represent the averages across all runs for the benchmark image.
 
 | Model                           | iPhone 17 Pro <br /> [ms] | iPhone 16 Pro <br /> [ms] | iPhone SE 3 | Samsung Galaxy S24 <br /> [ms] | OnePlus 12 <br /> [ms] |
@@ -145,7 +147,7 @@ Average time to synthesize speech from an input text of approximately 60 tokens,
 ## Text Embeddings
 
 :::note
-Benchmark times for text embeddings are highly dependent on the sentence length. The numbers above are based on a sentence of around 80 tokens. For shorter or longer sentences, inference time may vary accordingly.
+Benchmark times for text embeddings are highly dependent on the sentence length. The numbers below are based on a sentence of around 80 tokens. For shorter or longer sentences, inference time may vary accordingly.
 :::
 
 | Model                      | iPhone 17 Pro (XNNPACK) [ms] | OnePlus 12 (XNNPACK) [ms] |
@@ -194,7 +196,7 @@ slower for very large images, which can increase total time.
 
 ## Instance Segmentation
 
-:::info
+:::note
 Times presented in the tables are measured for YOLO models with input size equal to 512. Other input sizes may yield slower or faster inference times. RF-DETR Nano Seg uses a fixed resolution of 312×312.
 :::
 

docs/versioned_docs/version-0.8.x/02-benchmarks/memory-usage.md

@@ -20,7 +20,7 @@ before model initialization.
 ## Object Detection
 
 :::note
-Data presented for YOLO models is based on inference with forward_640 method.
+Data presented for YOLO models is based on inference with `forward_640` method.
 :::
 
 | Model / Device                                | iPhone 17 Pro [MB] | Google Pixel 10 [MB] |
@@ -114,20 +114,20 @@ The reported memory usage values include the memory footprint of the Phonemis pa
 
 ## Semantic Segmentation
 
-:::info
+:::note
 Data presented in the following sections is based on inference with non-resized
 output. When resize is enabled, expect higher memory usage and inference time
 with higher resolutions.
 :::
 
-| Model / Device              | iPhone 17 Pro [MB] | OnePlus 12 [MB] |
-| --------------------------- | :----------------: | :-------------: |
-| DEELABV3_RESNET50 (XNNPACK) |        660         |       930       |
+| Model / Device               | iPhone 17 Pro [MB] | OnePlus 12 [MB] |
+| ---------------------------- | :----------------: | :-------------: |
+| DEEPLABV3_RESNET50 (XNNPACK) |        660         |       930       |
 
 ## Instance Segmentation
 
-:::info
-Data presented in the following sections is based on inference with forward_640 method.
+:::note
+Data presented in the following sections is based on inference with `forward_640` method.
 :::
 
 | Model / Device             | iPhone 17 Pro [MB] | OnePlus 12 [MB] |