Rm: blog link, 7x

README.md

@@ -56,7 +56,7 @@ This repository includes package APIs along with examples to quickly get started
 Sparsification is the process of taking a trained deep learning model and removing redundant information from the overprecise and over-parameterized network resulting in a faster and smaller model.
 Techniques for sparsification are all encompassing including everything from inducing sparsity using [pruning](https://neuralmagic.com/blog/pruning-overview/) and [quantization](https://arxiv.org/abs/1609.07061) to enabling naturally occurring sparsity using [activation sparsity](http://proceedings.mlr.press/v119/kurtz20a.html) or [winograd/FFT](https://arxiv.org/abs/1509.09308). 
 When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics.
-For example, pruning plus quantization can give over [7x improvements in performance](https://neuralmagic.com/blog/benchmark-resnet50-with-deepsparse) while recovering to nearly the same baseline accuracy.
+For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy.
 
 The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches.
 Recipes encode the directions for how to sparsify a model into a simple, easily editable format.

docs/source/index.rst

@@ -62,7 +62,7 @@ Sparsification
 Sparsification is the process of taking a trained deep learning model and removing redundant information from the overprecise and over-parameterized network resulting in a faster and smaller model.
 Techniques for sparsification are all encompassing including everything from inducing sparsity using `pruning <https://neuralmagic.com/blog/pruning-overview/>`_ and `quantization <https://arxiv.org/abs/1609.07061>`_ to enabling naturally occurring sparsity using `activation sparsity <http://proceedings.mlr.press/v119/kurtz20a.html>`_ or `winograd/FFT <https://arxiv.org/abs/1509.09308>`_.
 When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics.
-For example, pruning plus quantization can give over `7x improvements in performance <https://neuralmagic.com/blog/benchmark-resnet50-with-deepsparse>`_ while recovering to nearly the same baseline accuracy.
+For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy.
 
 The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches.
 Recipes encode the directions for how to sparsify a model into a simple, easily editable format.
@@ -131,4 +131,4 @@ Additionally, more information can be found via
 
     Bugs, Feature Requests <https://github.com/neuralmagic/deepsparse/issues>
     Support, General Q&A <https://github.com/neuralmagic/deepsparse/discussions>
-    Neural Magic Docs <https://docs.neuralmagic.com>
+    Neural Magic Docs <https://docs.neuralmagic.com>