README 8

README.md

@@ -17,7 +17,7 @@ Once optimized, efficient distributed training becomes practical regardless of w
 
 Lightning Data supports **images, text, video, audio, geo-spatial, and multimodal data** types, is already adopted by frameworks such as [Lit-GPT](https://github.com/Lightning-AI/lit-gpt/blob/main/pretrain/tinyllama.py) to pretrain LLMs and integrates smoothly with [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), [Lightning Fabric](https://lightning.ai/docs/fabric/stable/), and [PyTorch](https://pytorch.org/docs/stable/index.html).
 
-[Runnable templates](#runnable-templates) published on the [Lightning.AI Platform](https://lightning.ai) are available at the end, they are fully reproducible with 1-click.
+[Runnable templates](#runnable-templates) published on the [Lightning.AI Platform](https://lightning.ai) are available at the end, **reproducible in 1-click**.
 
 ### Table of Contents
 
@@ -37,7 +37,7 @@ Lightning Data supports **images, text, video, audio, geo-spatial, and multimoda
 
 ## Installation
 
-Lightning Data can be installed with `pip`:
+Install **Lightning Data** with `pip`
 
 ```bash
 pip install litdata
@@ -47,7 +47,7 @@ pip install litdata
 
 ### 1. Prepare Your Data
 
-Convert your raw dataset into Lightning Streaming Format using the `optimize` operator.
+Convert your raw dataset into **Lightning Data Optimized Streaming Format** using the `optimize` operator.
 
 Here is an example with some random images. 
 
@@ -82,7 +82,7 @@ The `optimize` operator supports any data structures and types. Serialize whatev
 
 ### 2. Upload Your Data to Cloud Storage
 
-Cloud providers such as [AWS](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html), [Google Cloud](https://cloud.google.com/storage/docs/uploading-objects?hl=en#upload-object-cli), [Azure](https://learn.microsoft.com/en-us/azure/import-export/storage-import-export-data-to-files?tabs=azure-portal-preview), etc.. provide command line client to upload your data to their storage.
+Cloud providers such as [AWS](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html), [Google Cloud](https://cloud.google.com/storage/docs/uploading-objects?hl=en#upload-object-cli), [Azure](https://learn.microsoft.com/en-us/azure/import-export/storage-import-export-data-to-files?tabs=azure-portal-preview) provide command line client to upload your data to their storage.
 
 Here is how to upload the optimized dataset using the [AWS CLI](https://aws.amazon.com/s3) to [AWS S3](https://aws.amazon.com/s3/).