Update README.md

README.md

@@ -1,31 +1,31 @@
----
-license: llama3.2
----
-
-# Introduction
-
-This repository hosts the **LLaMa 3.2** models for the [React Native ExecuTorch](https://www.npmjs.com/package/react-native-executorch) library. It includes both the **1B** and **3B** versions of the LLaMa model, as well as their **quantized** versions in `.pte` format, ready for use in the **ExecuTorch** runtime.
-
-If you'd like to run these models in your own ExecuTorch runtime, refer to the [official documentation](https://pytorch.org/executorch/stable/index.html) for setup instructions.
-
-## Compatibility
-
-If you intend to use this model outside of React Native ExecuTorch, make sure your runtime is compatible with the **ExecuTorch** version used to export the `.pte` files. For more details, see the compatibility note in the [ExecuTorch GitHub repository](https://github.com/pytorch/executorch/blob/11d1742fdeddcf05bc30a6cfac321d2a2e3b6768/runtime/COMPATIBILITY.md?plain=1#L4). If you work with React Native ExecuTorch, the constants from the library will  guarantee compatibility with runtime used behind the scenes.
-
-These models were exported using commit `cbfdf78f8` and **no forward compatibility** is guaranteed. Older versions of the runtime may not work with these files.
-
-### Repository Structure
-
-The repository is organized into two main directories:
-
-- `llama-3.2-1B`
-- `llama-3.2-3B`
-
-Each directory contains different versions of the model, including **QLoRa**, **SpinQuant**, and the **original** models.
-
-- The `.pte` file should be passed to the `modelSource` parameter.
-- The corresponding `.bin` file should be used for `tokenizerSource`.
-
-If you wish to export the model yourself, you’ll need to obtain model weights and the `params.json` file from the official repositories, which can be found [here](https://huggingface.co/collections/meta-llama/llama-32-66f448ffc8c32f949b04c8cf). 
-
-For the **best performance-to-quality ratio**, we highly recommend using the **QLoRa** version, which is optimized for speed without sacrificing too much on model quality.
+---
+license: llama3.2
+---
+
+# Introduction
+
+This repository hosts the **LLaMa 3.2** models for the [React Native ExecuTorch](https://www.npmjs.com/package/react-native-executorch) library. It includes both the **1B** and **3B** versions of the LLaMa model, as well as their **quantized** versions in `.pte` format, ready for use in the **ExecuTorch** runtime.
+
+If you'd like to run these models in your own ExecuTorch runtime, refer to the [official documentation](https://pytorch.org/executorch/stable/index.html) for setup instructions.
+
+## Compatibility
+
+If you intend to use this model outside of React Native ExecuTorch, make sure your runtime is compatible with the **ExecuTorch** version used to export the `.pte` files. For more details, see the compatibility note in the [ExecuTorch GitHub repository](https://github.com/pytorch/executorch/blob/11d1742fdeddcf05bc30a6cfac321d2a2e3b6768/runtime/COMPATIBILITY.md?plain=1#L4). If you work with React Native ExecuTorch, the constants from the library will  guarantee compatibility with runtime used behind the scenes.
+
+These models were exported using commit `fe20be98c` and **no forward compatibility** is guaranteed. Older versions of the runtime may not work with these files.
+
+### Repository Structure
+
+The repository is organized into two main directories:
+
+- `llama-3.2-1B`
+- `llama-3.2-3B`
+
+Each directory contains different versions of the model, including **QLoRa**, **SpinQuant**, and the **original** models.
+
+- The `.pte` file should be passed to the `modelSource` parameter.
+- The corresponding `.bin` file should be used for `tokenizerSource`.
+
+If you wish to export the model yourself, you’ll need to obtain model weights and the `params.json` file from the official repositories, which can be found [here](https://huggingface.co/collections/meta-llama/llama-32-66f448ffc8c32f949b04c8cf). 
+
+For the **best performance-to-quality ratio**, we highly recommend using the **QLoRa** version, which is optimized for speed without sacrificing too much on model quality.