Update README with model author names and speedup numbers. (#3)

- Update README with model author names and speedup numbers. (a1d32b0dcbcf8a71f717a2752205220e5292e4d1)


Co-authored-by: Eugenia Iofinova <jen@users.noreply.huggingface.co>

README.md

@@ -5,10 +5,19 @@ inference: false
 tags:
 - deepsparse
 ---
-This is a [SparseML](https://github.com/neuralmagic/sparseml) quantized version of https://huggingface.co/laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K that is ready to use with [DeepSparse](https://github.com/neuralmagic/deepsparse). 
+This is a [SparseML](https://github.com/neuralmagic/sparseml) quantized version of
+https://huggingface.co/laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K that is ready to use with
+the [DeepSparse](https://github.com/neuralmagic/deepsparse) CPU inference engine. 
 It achieves **71.1%** zero-shot top-1 accuracy on ImageNet and **95.6%** zero-shot top-1 accuracy on Imagenette. 
 For comparison the dense version (the original model) achieves **72.8%** on ImageNet and **95.7%** on Imagenette.
 
+On an Intel avx512 CPU machine with 64 cores and VNNI support, this model achieves a **2.35x** speedup for textual
+and **2.84x** speedup for visual inputs as compared to the full-precision model. With a batch size of 64,
+the throughput was measured as **1230 items/sec** for images and **2009 items/sec** for text.
+
+This model and the example pipeline were created by Eugenia Iofinova, Michael Goin, Chris Wendler, and Dan Alistarh.
+Special thanks to Abhinav Agarwalla and Alexandre Marques for technical support with parts of the project.
+
 Notebook for basic usage: [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ZvU9ZSHJKSeJyH5bgxo_A-GSVIUcSt2E?usp=sharing)
 Notebook for Imagenette evaluation: [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1-Duq0YNtjzOnmuXCYo-5DDiOzeCItXpN?usp=sharing)