From 0afa5071bd84e44301750fdc594e33db102cf374 Mon Sep 17 00:00:00 2001
From: Nino Risteski <95188570+NinoRisteski@users.noreply.github.com>
Date: Fri, 1 Sep 2023 21:27:01 +0200
Subject: [PATCH] Update model_memory_anatomy.md (#25896)

typo fixes
---
 docs/source/en/model_memory_anatomy.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/en/model_memory_anatomy.md b/docs/source/en/model_memory_anatomy.md
index d53a16a95e..031bf5e66e 100644
--- a/docs/source/en/model_memory_anatomy.md
+++ b/docs/source/en/model_memory_anatomy.md
@@ -76,7 +76,7 @@ GPU memory occupied: 0 MB.
 
 That looks good: the GPU memory is not occupied as we would expect before we load any models. If that's not the case on 
 your machine make sure to stop all processes that are using GPU memory. However, not all free GPU memory can be used by 
-the user. When a model is loaded to the GPU also the kernels are loaded which can take up 1-2GB of memory. To see how 
+the user. When a model is loaded to the GPU the kernels are also loaded, which can take up 1-2GB of memory. To see how 
 much it is we load a tiny tensor into the GPU which triggers the kernels to be loaded as well.
 
 ```py
@@ -105,7 +105,7 @@ how much space just the weights use.
 GPU memory occupied: 2631 MB.
 ```
 
-We can see that the model weights alone take up 1.3 GB of the GPU memory. The exact number depends on the specific 
+We can see that the model weights alone take up 1.3 GB of GPU memory. The exact number depends on the specific 
 GPU you are using. Note that on newer GPUs a model can sometimes take up more space since the weights are loaded in an 
 optimized fashion that speeds up the usage of the model. Now we can also quickly check if we get the same result 
 as with `nvidia-smi` CLI: