Update links in the documentation (#483)

## Description

Updating links to the react-native-executorch repo and executorch
documentation in the versioned docs + correcting sendMessage signature.

### Type of change

- [x] 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


### Screenshots


### Related issues

Closes #478

### Checklist

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

### Additional notes

Links to
[github.com/pytorch/executorch/](https://github.com/pytorch/executorch)
refer to the latest release, and links to
[pytorch.org/vision/](https://docs.pytorch.org/vision/stable/index.html)
refer to the most recent stable version as in executorch documentation.

Author: a-szymanska

docs/docs/02-hooks/01-natural-language-processing/useLLM.md

@@ -24,7 +24,7 @@ description: "Learn how to use LLMs in your React Native applications with React
 React Native ExecuTorch supports a variety of LLMs (checkout our [HuggingFace repository](https://huggingface.co/software-mansion) for model already converted to ExecuTorch format) including Llama 3.2. Before getting started, you’ll need to obtain the .pte binary—a serialized model, the tokenizer and tokenizer config JSON files. There are various ways to accomplish this:
 
 - For your convenience, it's best if you use models exported by us, you can get them from our [HuggingFace repository](https://huggingface.co/software-mansion). You can also use [constants](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/src/constants/modelUrls.ts) shipped with our library.
-- Follow the official [tutorial](https://github.com/pytorch/executorch/blob/fe20be98c/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md) made by ExecuTorch team to build the model and tokenizer yourself.
+- Follow the official [tutorial](https://github.com/pytorch/executorch/blob/release/0.7/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md) made by ExecuTorch team to build the model and tokenizer yourself.
 
 :::danger
 Lower-end devices might not be able to fit LLMs into memory. We recommend using quantized models to reduce the memory footprint.
@@ -80,7 +80,7 @@ The code snippet above fetches the model from the specified URL, loads it into m
 | `downloadProgress` | `number`                                                                    | Represents the download progress as a value between 0 and 1, indicating the extent of the model file retrieval.                                 |
 | `error`            | <code>string &#124; null</code>                                             | Contains the error message if the model failed to load.                                                                                         |
 | `configure`        | `({ chatConfig?: Partial<ChatConfig>, toolsConfig?: ToolsConfig }) => void` | Configures chat and tool calling. See more details in [configuring the model](#configuring-the-model).                                          |
-| `sendMessage`      | `(message: string, tools?: LLMTool[]) => Promise<void>`                     | Function to add user message to conversation. After model responds, `messageHistory` will be updated with both user message and model response. |
+| `sendMessage`      | `(message: string) => Promise<void>`                                        | Function to add user message to conversation. After model responds, `messageHistory` will be updated with both user message and model response. |
 | `deleteMessage`    | `(index: number) => void`                                                   | Deletes all messages starting with message on `index` position. After deletion `messageHistory` will be updated.                                |
 | `messageHistory`   | `Message[]`                                                                 | History containing all messages in conversation. This field is updated after model responds to `sendMessage`.                                   |
 

docs/docs/02-hooks/02-computer-vision/useClassification.md

@@ -87,7 +87,7 @@ function App() {
 
 | Model                                                                                                           | Number of classes | Class list                                                                                                                                                                    |
 | --------------------------------------------------------------------------------------------------------------- | ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [efficientnet_v2_s](https://pytorch.org/vision/0.20/models/generated/torchvision.models.efficientnet_v2_s.html) | 1000              | [ImageNet1k_v1](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/common/rnexecutorch/models/classification/Constants.h) |
+| [efficientnet_v2_s](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_v2_s.html) | 1000              | [ImageNet1k_v1](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/common/rnexecutorch/models/classification/Constants.h) |
 
 ## Benchmarks
 

docs/docs/02-hooks/02-computer-vision/useImageSegmentation.md

@@ -87,7 +87,7 @@ function App() {
 
 | Model                                                                                                                          | Number of classes | Class list                                                                                                                                            |
 | ------------------------------------------------------------------------------------------------------------------------------ | ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [deeplabv3_resnet50](https://pytorch.org/vision/0.20/models/generated/torchvision.models.segmentation.deeplabv3_resnet50.html) | 21                | [DeeplabLabel](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/src/types/imageSegmentation.ts) |
+| [deeplabv3_resnet50](https://pytorch.org/vision/stable/models/generated/torchvision.models.segmentation.deeplabv3_resnet50.html) | 21                | [DeeplabLabel](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/src/types/imageSegmentation.ts) |
 
 ## Benchmarks
 

docs/docs/02-hooks/02-computer-vision/useObjectDetection.md

@@ -126,7 +126,7 @@ function App() {
 
 | Model                                                                                                                                                                                                               | Number of classes | Class list                                                                                                                                                             |
 | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [SSDLite320 MobileNetV3 Large](https://pytorch.org/vision/main/models/generated/torchvision.models.detection.ssdlite320_mobilenet_v3_large.html#torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights) | 91                | [COCO](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/common/rnexecutorch/models/object_detection/Constants.h) |
+| [SSDLite320 MobileNetV3 Large](https://pytorch.org/vision/stable/models/generated/torchvision.models.detection.ssdlite320_mobilenet_v3_large.html#torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights) | 91                | [COCO](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/common/rnexecutorch/models/object_detection/Constants.h) |
 
 ## Benchmarks
 

docs/docs/02-hooks/03-executorch-bindings/useExecutorchModule.md

@@ -52,7 +52,7 @@ The `ETInput` type defines the typed arrays that can be used as inputs in the `f
 
 ## Errors
 
-All functions provided by the `useExecutorchModule` hook are asynchronous and may throw an error. The `ETError` enum includes errors [defined by the ExecuTorch team](https://github.com/pytorch/executorch/blob/main/runtime/core/error.h) and additional errors specified by our library.
+All functions provided by the `useExecutorchModule` hook are asynchronous and may throw an error. The `ETError` enum includes errors [defined by the ExecuTorch team](https://github.com/pytorch/executorch/blob/release/0.7/runtime/core/error.h) and additional errors specified by our library.
 
 ## Performing inference
 

docs/docs/03-typescript-api/01-natural-language-processing/LLMModule.md

@@ -35,7 +35,7 @@ await llm.load({
 // Running the model
 await llm.sendMessage('Hello, World!');
 
-// Interrupting the model (to actually interrupt the generation it has to be called when sendMessage or forward is running)
+// Interrupting the model (to actually interrupt the generation it has to be called when sendMessage or generate is running)
 llm.interrupt();
 
 // Deleting the model from memory

docs/versioned_docs/version-0.1.x/guides/running-llms.md

@@ -5,9 +5,9 @@ sidebar_position: 1
 
 React Native ExecuTorch supports Llama 3.2 models, including quantized versions. Before getting started, you’ll need to obtain the .pte binary—a serialized model—and the tokenizer. There are various ways to accomplish this:
 
-- For your convienience, it's best if you use models exported by us, you can get them from our hugging face repository. You can also use [constants](https://github.com/software-mansion/react-native-executorch/tree/main/src/constants/modelUrls.ts) shipped with our library.
+- For your convienience, it's best if you use models exported by us, you can get them from our hugging face repository. You can also use [constants](https://github.com/software-mansion/react-native-executorch/blob/v0.1.3/src/modelUrls.ts) shipped with our library.
 - If you want to export model by yourself,you can use a Docker image that we've prepared. To see how it works, check out [exporting Llama](./exporting-llama.mdx)
-- Follow the official [tutorial](https://github.com/pytorch/executorch/blob/fe20be98c/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md) made by ExecuTorch team to build the model and tokenizer yourself
+- Follow the official [tutorial](https://github.com/pytorch/executorch/blob/release/0.7/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md) made by ExecuTorch team to build the model and tokenizer yourself
 
 ## Initializing
 

docs/versioned_docs/version-0.2.x/computer-vision/useClassification.mdx

@@ -10,7 +10,7 @@ Usually, the class with the highest probability is the one that is assigned to a
 :::
 
 :::caution
-It is recommended to use models provided by us, which are available at our [Hugging Face repository](https://huggingface.co/software-mansion/react-native-executorch-efficientnet-v2-s). You can also use [constants](https://github.com/software-mansion/react-native-executorch/tree/main/src/constants/modelUrls.ts) shipped with our library
+It is recommended to use models provided by us, which are available at our [Hugging Face repository](https://huggingface.co/software-mansion/react-native-executorch-efficientnet-v2-s). You can also use [constants](https://github.com/software-mansion/react-native-executorch/blob/v0.2.0/src/constants/modelUrls.ts) shipped with our library
 :::
 
 ## Reference
@@ -99,4 +99,4 @@ function App() {
 
 | Model                                                                                                           | Number of classes | Class list                                                                                                                                                                 |
 | --------------------------------------------------------------------------------------------------------------- | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [efficientnet_v2_s](https://pytorch.org/vision/0.20/models/generated/torchvision.models.efficientnet_v2_s.html) | 1000              | [ImageNet1k_v1](https://github.com/software-mansion/react-native-executorch/blob/main/android/src/main/java/com/swmansion/rnexecutorch/models/classification/Constants.kt) |
+| [efficientnet_v2_s](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_v2_s.html) | 1000              | [ImageNet1k_v1](https://github.com/software-mansion/react-native-executorch/blob/v0.2.0/android/src/main/java/com/swmansion/rnexecutorch/models/classification/Constants.kt) |

docs/versioned_docs/version-0.2.x/computer-vision/useObjectDetection.mdx

@@ -7,7 +7,7 @@ Object detection is a computer vision technique that identifies and locates obje
 `useObjectDetection` is a hook that allows you to seamlessly integrate object detection into your React Native applications.
 
 :::caution
-It is recommended to use models provided by us, which are available at our [Hugging Face repository](https://huggingface.co/software-mansion/react-native-executorch-ssdlite320-mobilenet-v3-large). You can also use [constants](https://github.com/software-mansion/react-native-executorch/blob/69802ee1ca161d9df00def1dabe014d36341cfa9/src/constants/modelUrls.ts#L28) shipped with our library.
+It is recommended to use models provided by us, which are available at our [Hugging Face repository](https://huggingface.co/software-mansion/react-native-executorch-ssdlite320-mobilenet-v3-large). You can also use [constants](https://github.com/software-mansion/react-native-executorch/blob/v0.2.0/src/constants/modelUrls.ts) shipped with our library.
 :::
 
 ## Reference
@@ -124,4 +124,4 @@ function App() {
 
 | Model                                                                                                           | Number of classes | Class list                                                                                                                                                                 |
 | --------------------------------------------------------------------------------------------------------------- | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [SSDLite320 MobileNetV3 Large](https://pytorch.org/vision/main/models/generated/torchvision.models.detection.ssdlite320_mobilenet_v3_large.html#torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights) | 91              | [COCO](https://github.com/software-mansion/react-native-executorch/blob/69802ee1ca161d9df00def1dabe014d36341cfa9/src/types/object_detection.ts#L14) |
+| [SSDLite320 MobileNetV3 Large](https://pytorch.org/vision/stable/models/generated/torchvision.models.detection.ssdlite320_mobilenet_v3_large.html#torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights) | 91              | [COCO](https://github.com/software-mansion/react-native-executorch/blob/v0.2.0/src/types/object_detection.ts) |

docs/versioned_docs/version-0.2.x/llms/running-llms.md

@@ -5,9 +5,9 @@ sidebar_position: 1
 
 React Native ExecuTorch supports Llama 3.2 models, including quantized versions. Before getting started, you’ll need to obtain the .pte binary—a serialized model—and the tokenizer. There are various ways to accomplish this:
 
-- For your convienience, it's best if you use models exported by us, you can get them from our hugging face repository. You can also use [constants](https://github.com/software-mansion/react-native-executorch/tree/main/src/constants/modelUrls.ts) shipped with our library.
+- For your convienience, it's best if you use models exported by us, you can get them from our hugging face repository. You can also use [constants](https://github.com/software-mansion/react-native-executorch/blob/v0.2.0/src/constants/modelUrls.ts) shipped with our library.
 - If you want to export model by yourself,you can use a Docker image that we've prepared. To see how it works, check out [exporting Llama](./exporting-llama)
-- Follow the official [tutorial](https://github.com/pytorch/executorch/blob/fe20be98c/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md) made by ExecuTorch team to build the model and tokenizer yourself
+- Follow the official [tutorial](https://github.com/pytorch/executorch/blob/release/0.7/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md) made by ExecuTorch team to build the model and tokenizer yourself
 
 ## Initializing