DeepSpeed Compression announcement

README.md

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 [![Downloads](https://pepy.tech/badge/deepspeed/month)](https://pepy.tech/project/deepspeed)
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 ## Latest News
-* [2022/06/22] DeepSpeed Compression: 50x model size reduction via [XTC](https://arxiv.org/abs/2206.01859) and 5000x compression cost reduction via [ZeroQuant](https://arxiv.org/abs/2206.01861). Stay tuned for upcoming code release!
+* [2022/07/20] [DeepSpeed Compression: A composable library for extreme compression and zero-cost quantization](https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/)
+    * [Tutorial](https://www.deepspeed.ai/tutorials/model-compression/) and [Code examples](https://github.com/microsoft/DeepSpeedExamples/tree/master/model_compression).
+    * 50x model size reduction via [XTC](https://arxiv.org/abs/2206.01859) and 5000x compression cost reduction via [ZeroQuant](https://arxiv.org/abs/2206.01861).
 * [2022/03/21] [Supporting efficient large model training on AMD Instinct GPUs with DeepSpeed](https://cloudblogs.microsoft.com/opensource/2022/03/21/supporting-efficient-large-model-training-on-amd-instinct-gpus-with-deepspeed/)
 * [2022/03/07] [Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam](https://www.deepspeed.ai/tutorials/zero-one-adam/)
 * [2022/01/19] [DeepSpeed: Advancing MoE inference and training to power next-generation AI scale](https://www.microsoft.com/en-us/research/blog/deepspeed-advancing-moe-inference-and-training-to-power-next-generation-ai-scale/)

docs/_tutorials/model-compression.md

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 **What is DeepSpeed Compression:** DeepSpeed Compression is a library purposely built to make it easy to compress models for researchers and practitioners while delivering faster speed, smaller model size, and significantly reduced compression cost.
 
-**Why use DeepSpeed Compression:**  DeepSpeed Compression offers novel state-of-the-art compression techniques to achieve faster model compression with better model quality and lower compression cost. DeepSpeed Compression also takes an end-to-end approach to improve the computation efficiency of compressed models via a highly optimized inference engine. Furthermore, our library has multiple built-in state-of-the-art compression methods. It supports the synergistic composition of these methods and the system optimizations, offering the best of both worlds while allowing a seamless and easy-to-use pipeline for efficient DL model inference. We highly recommend you also to read our blog to learn more about (at a high level) why we build DeepSpeed Compression and what benefits it provides to users.
-<!-- TODO: add blog link after it's live. -->
+**Why use DeepSpeed Compression:**  DeepSpeed Compression offers novel state-of-the-art compression techniques to achieve faster model compression with better model quality and lower compression cost. DeepSpeed Compression also takes an end-to-end approach to improve the computation efficiency of compressed models via a highly optimized inference engine. Furthermore, our library has multiple built-in state-of-the-art compression methods. It supports the synergistic composition of these methods and the system optimizations, offering the best of both worlds while allowing a seamless and easy-to-use pipeline for efficient DL model inference. We highly recommend you also to read [our blog](https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/) to learn more about (at a high level) why we build DeepSpeed Compression and what benefits it provides to users.
 
 **How to use DeepSpeed Compression:** The first section General Tutorial will describe the compression methods supported by the library. The following sections will describe our research work on how to compose different compression methods to perform [zero-cost quantization (ZeroQuant)](#2-tutorial-for-zeroquant-efficient-and-affordable-post-training-quantization) and [extreme compression (XTC)](#3-tutorial-for-xtc-simple-yet-effective-compression-pipeline-for-extreme-compression). Unless otherwise stated, experiment results listed below are based on NVIDIA A100 GPU, and we observe slightly different result numbers when using different GPU hardwares.
 

docs/index.md

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 ---
 
-* [2022/06/22] DeepSpeed Compression: 50x model size reduction via [XTC](https://arxiv.org/abs/2206.01859) and 5000x compression cost reduction via [ZeroQuant](https://arxiv.org/abs/2206.01861). Stay tuned for upcoming code release!
+* [2022/07/20] [DeepSpeed Compression: A composable library for extreme compression and zero-cost quantization](https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/)
+    * [Tutorial](https://www.deepspeed.ai/tutorials/model-compression/) and [Code examples](https://github.com/microsoft/DeepSpeedExamples/tree/master/model_compression).
+    * 50x model size reduction via [XTC](https://arxiv.org/abs/2206.01859) and 5000x compression cost reduction via [ZeroQuant](https://arxiv.org/abs/2206.01861).
 * [2022/03/21] [Supporting efficient large model training on AMD Instinct GPUs with DeepSpeed](https://cloudblogs.microsoft.com/opensource/2022/03/21/supporting-efficient-large-model-training-on-amd-instinct-gpus-with-deepspeed/)
 * [2022/03/07] [Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam](https://www.deepspeed.ai/tutorials/zero-one-adam/)
 * [2022/01/19] [DeepSpeed: Advancing MoE inference and training to power next-generation AI scale](https://www.microsoft.com/en-us/research/blog/deepspeed-advancing-moe-inference-and-training-to-power-next-generation-ai-scale/)