diff --git a/mixtral/README.md b/mixtral/README.md
index a3af2b66..e46976cb 100644
--- a/mixtral/README.md
+++ b/mixtral/README.md
@@ -2,8 +2,7 @@
 
 Run the Mixtral[^mixtral] 8x7B mixture-of-experts (MoE) model in MLX on Apple silicon.
 
-Note, this model needs a machine with substantial RAM (>= 128GB) to run in
-16-bit precision. 
+Note, for 16-bit precision this model needs a machine with substantial RAM (~100GB) to run.
 
 ### Setup
 
@@ -15,7 +14,7 @@ For example with Homebrew:
 brew install git-lfs
 ```
 
-Download the models from HugginFace:
+Download the models from HuggingFace:
 
 ```
 git clone https://huggingface.co/someone13574/mixtral-8x7b-32kseqlen
@@ -27,7 +26,8 @@ cd mixtral-8x7b-32kseqlen/
 cat consolidated.00.pth-split0 consolidated.00.pth-split1 consolidated.00.pth-split2 consolidated.00.pth-split3 consolidated.00.pth-split4 consolidated.00.pth-split5 consolidated.00.pth-split6 consolidated.00.pth-split7 consolidated.00.pth-split8 consolidated.00.pth-split9 consolidated.00.pth-split10 > consolidated.00.pth
 ```
 
-Now from `mlx-exmaples/mixtral` conver the weights to NumPy so MLX can read them:
+Now from `mlx-exmaples/mixtral` conver and save the weights as NumPy arrays so
+MLX can read them:
 
 ```
 python convert.py --model_path mixtral-8x7b-32kseqlen/
@@ -49,4 +49,4 @@ As easy as:
 python mixtral.py --model_path mixtral mixtral-8x7b-32kseqlen/
 ```
 
-[^mixtral] Refer to Mistral's [blog post](https://mistral.ai/news/mixtral-of-experts/) for more details.
+[^mixtral]: Refer to Mistral's [blog post](https://mistral.ai/news/mixtral-of-experts/) for more details.