diff --git a/README.md b/README.md
index ac124690ae..77b5f0ada5 100644
--- a/README.md
+++ b/README.md
@@ -3,18 +3,26 @@
 **Note: This repository is currently under heavy development - if you have suggestions on the API or use-cases you'd like to be covered, please open an github issue**
 
 ## Introduction
-
 torchao is a PyTorch native library for optimizing your models using lower precision dtypes, techniques like quantization and sparsity and performant kernels.
 
-## Our Goals
-torchao embodies PyTorch’s design philosophy [details](https://pytorch.org/docs/stable/community/design.html), especially "usability over everything else". Our vision for this repository is the following:
+## Get Started
+To try out our APIs, you can check out API examples in [quantization](./torchao/quantization) (including `autoquant`), [sparsity](./torchao/sparsity), [dtypes](./torchao/dtypes).
 
-* Composability: Native solutions for optimization techniques that compose with both `torch.compile` and `FSDP` 
-    * For example, for QLoRA for new dtypes support
-* Interoperability: Work with the rest of the PyTorch ecosystem such as torchtune, gpt-fast and ExecuTorch
-* Transparent Benchmarks: Regularly run performance benchmarking of our APIs across a suite of Torchbench models and across hardware backends
-* Heterogeneous Hardware: Efficient kernels that can run on CPU/GPU based server (w/ torch.compile) and mobile backends (w/ ExecuTorch).
-* Infrastructure Support: Release packaging solution for kernels and a CI/CD setup that runs these kernels on different backends. 
+## Installation
+**Note: this library makes liberal use of several new features in pytorch, its recommended to use it with the current nightly or latest stable version of PyTorch.**
+
+1. From PyPI:
+```Shell
+pip install torchao
+```
+
+2. From Source:
+
+```Shell
+git clone https://github.com/pytorch-labs/ao
+cd ao
+pip install -e .
+```
 
 ## Key Features
 The library provides
@@ -28,6 +36,17 @@ The library provides
 3. [Sparsity algorithms](./torchao/sparsity) such as Wanda that help improve accuracy of sparse networks
 4. Integration with other PyTorch native libraries like [torchtune](https://github.com/pytorch/torchtune) and [ExecuTorch](https://github.com/pytorch/executorch)
 
+
+## Our Goals
+torchao embodies PyTorch’s design philosophy [details](https://pytorch.org/docs/stable/community/design.html), especially "usability over everything else". Our vision for this repository is the following:
+
+* Composability: Native solutions for optimization techniques that compose with both `torch.compile` and `FSDP` 
+    * For example, for QLoRA for new dtypes support
+* Interoperability: Work with the rest of the PyTorch ecosystem such as torchtune, gpt-fast and ExecuTorch
+* Transparent Benchmarks: Regularly run performance benchmarking of our APIs across a suite of Torchbench models and across hardware backends
+* Heterogeneous Hardware: Efficient kernels that can run on CPU/GPU based server (w/ torch.compile) and mobile backends (w/ ExecuTorch).
+* Infrastructure Support: Release packaging solution for kernels and a CI/CD setup that runs these kernels on different backends. 
+
 ## Interoperability with PyTorch Libraries
 
 torchao has been integrated with other repositories to ease usage
@@ -42,27 +61,6 @@ Our kernels have has been used to achieve SOTA inference performance on
 2. Language models with [gpt-fast](pytorch.org/blog/accelerating-generative-ai-2)
 3. Diffusion models with [sd-fast](pytorch.org/blog/accelerating-generative-ai-3)
 
-
-## Installation
-
-**Note: this library makes liberal use of several new features in pytorch, its recommended to use it with the current pytorch nightly if you want full feature coverage. If not, the subclass APIs may not work, though the module swap api's will still work.**
-
-1. From PyPI:
-```Shell
-pip install torchao
-```
-
-2. From Source:
-
-```Shell
-git clone https://github.com/pytorch-labs/ao
-cd ao
-pip install -e .
-```
-
-## Get Started
-To try out our APIs, you can check out API examples in [quantization](./torchao/quantization) (including `autoquant`), [sparsity](./torchao/sparsity), [dtypes](./torchao/dtypes).
-
 ## License
 
 `torchao` is released under the [BSD 3](https://github.com/pytorch-labs/ao/blob/main/LICENSE) license.