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typos in readme

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Awni Hannun 2023-12-12 08:41:28 -08:00
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mixtral/README.md
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@ -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.