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Merged
merged 2 commits into from
Apr 25, 2024
Merged

Fix minor grammatical corrections #179

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15556be
fix typos in README.md
aakashapoorv Apr 25, 2024
39b1c8e
Merge branch 'main' into fix-typo
msaroufim Apr 25, 2024
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6 changes: 3 additions & 3 deletions README.md
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# torchao: PyTorch Architecture Optimization

**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**
**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.
Expand All @@ -9,7 +9,7 @@ torchao is a PyTorch native library for optimizing your models using lower preci
To try out our APIs, you can check out API examples in [quantization](./torchao/quantization) (including `autoquant`), [sparsity](./torchao/sparsity), [dtypes](./torchao/dtypes).

## 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.**
**Note: this library makes liberal use of several new features in pytorch, it's recommended to use it with the current nightly or latest stable version of PyTorch.**

1. From PyPI:
```Shell
Expand Down Expand Up @@ -55,7 +55,7 @@ torchao has been integrated with other repositories to ease usage
* [Executorch](https://github.com/pytorch/executorch/tree/main/examples/models/llama2#quantization) is integrated with GPTQ for both 8da4w (int8 dynamic activation, with int4 weight) and int4 weight only quantization.

## Success stories
Our kernels have has been used to achieve SOTA inference performance on
Our kernels have been used to achieve SOTA inference performance on

1. Image segmentation models with [sam-fast](pytorch.org/blog/accelerating-generative-ai)
2. Language models with [gpt-fast](pytorch.org/blog/accelerating-generative-ai-2)
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