docs: Streamline docs (#1037)

## Description

This PR changes some general issue in our documentation:
- Removes duplicated Hire Us banner
- Correct information marker
- Unify tables (use checkmarks, make columns the same in benchmarks
section)
- Fix code snippets
- Fix typos

### Introduces a breaking change?

- [ ] Yes
- [x] No

### Type of change

- [ ] Bug fix (change which fixes an issue)
- [ ] New feature (change which adds functionality)
- [x] Documentation update (improves or adds clarity to existing
documentation)
- [ ] Other (chores, tests, code style improvements etc.)

### Tested on

- [ ] iOS
- [ ] Android

### Testing instructions

Build documentation locally via `yarn start` and check if everything is
corrected.

### Screenshots

<!-- Add screenshots here, if applicable -->

### Related issues

<!-- Link related issues here using #issue-number -->

### Checklist

- [ ] I have performed a self-review of my code
- [x] I have commented my code, particularly in hard-to-understand areas
- [x] I have updated the documentation accordingly
- [ ] My changes generate no new warnings

### Additional notes

<!-- Include any additional information, assumptions, or context that
reviewers might need to understand this PR. -->

---------

Co-authored-by: Bartosz Hanc <bartosz.hanc02@gmail.com>

Author: msluszniak

docs/docs/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
 
@@ -43,7 +43,7 @@ Installation is pretty straightforward, use your package manager of choice to in
 <Tabs>
   <TabItem value="npm" label="NPM">
 
-    ```
+    ```bash
     npm install react-native-executorch
     # For Expo projects
     npm install react-native-executorch-expo-resource-fetcher
@@ -54,19 +54,18 @@ Installation is pretty straightforward, use your package manager of choice to in
   </TabItem>
   <TabItem value="pnpm" label="PNPM">
 
-    ```
+    ```bash
     pnpm install react-native-executorch
     # For Expo projects
     pnpm install react-native-executorch-expo-resource-fetcher
     # For bare React Native projects
     pnpm install react-native-executorch-bare-resource-fetcher
-
     ```
 
   </TabItem>
   <TabItem value="yarn" label="YARN">
 
-    ```
+    ```bash
     yarn add react-native-executorch
     # For Expo projects
     yarn install react-native-executorch-expo-resource-fetcher
@@ -123,11 +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/docs/01-fundamentals/02-loading-models.md

@@ -44,15 +44,17 @@ initExecutorch({
 });
 ```
 
-**1. Load from React Native assets folder (For Files < 512MB)**
+## Loading
+
+### 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).
 
@@ -62,7 +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:
 
@@ -72,7 +74,7 @@ useExecutorchModule({
 });
 ```
 
-:::info
+:::note
 The downloaded files are stored in documents directory of your application.
 :::
 
@@ -85,10 +87,7 @@ Our library offers out-of-the-box support for multiple models. To make things ea
 The following code snippet demonstrates how to load model and tokenizer files using `useLLM` hook:
 
 ```typescript
-import { useLLM } from 'react-native-executorch';
+import { useLLM, LLAMA3_2_1B } from 'react-native-executorch';
 
-const llama = useLLM({
-  modelSource: 'https://.../llama3_2.pte',
-  tokenizerSource: require('../assets/tokenizer.bin'),
-});
+const llama = useLLM({ model: LLAMA3_2_1B });
 ```

docs/docs/02-benchmarks/inference-time.md

@@ -2,21 +2,19 @@
 title: Inference Time
 ---
 
-:::warning
+:::info
 Times presented in the tables are measured as consecutive runs of the model.
 Initial run times may be up to 2x longer due to model loading and
 initialization.
-:::
-
-## Classification
 
-:::info
 Inference times are measured directly from native C++ code, wrapping only the
 model's forward pass, excluding input-dependent pre- and post-processing (e.g.
 image resizing, normalization) and any overhead from React Native runtime.
 :::
 
-:::info
+## Classification
+
+:::note
 For this model all input images, whether larger or smaller, are resized before
 processing. Resizing is typically fast for small images but may be noticeably
 slower for very large images, which can increase total time.
@@ -31,19 +29,11 @@ slower for very large images, which can increase total time.
 
 ## Object Detection
 
-:::info
-Inference times are measured directly from native C++ code, wrapping only the
-model's forward pass, excluding input-dependent pre- and post-processing (e.g.
-image resizing, normalization) and any overhead from React Native runtime.
-:::
-
-:::info
+:::note
 For this model all input images, whether larger or smaller, are resized before
 processing. Resizing is typically fast for small images but may be noticeably
 slower for very large images, which can increase total time.
-:::
 
-:::warning
 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 uses a fixed resolution of 312×312.
 :::
 
@@ -61,13 +51,7 @@ Times presented in the tables are measured for YOLO models with input size equal
 
 ## Style Transfer
 
-:::info
-Inference times are measured directly from native C++ code, wrapping only the
-model's forward pass, excluding input-dependent pre- and post-processing (e.g.
-image resizing, normalization) and any overhead from React Native runtime.
-:::
-
-:::info
+:::note
 For this model all input images, whether larger or smaller, are resized before
 processing. Resizing is typically fast for small images but may be noticeably
 slower for very large images, which can increase total time.
@@ -107,7 +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] |
@@ -160,6 +146,10 @@ 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 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] |
 | -------------------------- | :--------------------------: | :-----------------------: |
 | ALL_MINILM_L6_V2           |              7               |            21             |
@@ -168,19 +158,9 @@ Average time to synthesize speech from an input text of approximately 60 tokens,
 | MULTI_QA_MPNET_BASE_DOT_V1 |              24              |            88             |
 | CLIP_VIT_BASE_PATCH32_TEXT |              14              |            39             |
 
-:::info
-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.
-:::
-
 ## Image Embeddings
 
-:::info
-Inference times are measured directly from native C++ code, wrapping only the
-model's forward pass, excluding input-dependent pre- and post-processing (e.g.
-image resizing, normalization) and any overhead from React Native runtime.
-:::
-
-:::info
+:::note
 For this model all input images, whether larger or smaller, are resized before
 processing. Resizing is typically fast for small images but may be noticeably
 slower for very large images, which can increase total time.
@@ -193,13 +173,7 @@ slower for very large images, which can increase total time.
 
 ## Semantic Segmentation
 
-:::info
-Inference times are measured directly from native C++ code, wrapping only the
-model's forward pass, excluding input-dependent pre- and post-processing (e.g.
-image resizing, normalization) and any overhead from React Native runtime.
-:::
-
-:::info
+:::note
 For this model all input images, whether larger or smaller, are resized before
 processing. Resizing is typically fast for small images but may be noticeably
 slower for very large images, which can increase total time.
@@ -222,10 +196,7 @@ slower for very large images, which can increase total time.
 
 ## Instance Segmentation
 
-:::warning
-Times presented in the tables are measured as consecutive runs of the model. Initial run times may be up to 2x longer due to model loading and initialization.
-:::
-:::warning
+:::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.
 :::