mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-07-12 20:51:13 +08:00
53 lines
1.4 KiB
Markdown
53 lines
1.4 KiB
Markdown
## Mixtral 8x7B
|
|
|
|
Run the Mixtral[^mixtral] 8x7B mixture-of-experts (MoE) model in MLX on Apple silicon.
|
|
|
|
Note, for 16-bit precision this model needs a machine with substantial RAM (~100GB) to run.
|
|
|
|
### Setup
|
|
|
|
Install [Git Large File
|
|
Storage](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage).
|
|
For example with Homebrew:
|
|
|
|
```
|
|
brew install git-lfs
|
|
```
|
|
|
|
Download the models from Hugging Face:
|
|
|
|
```
|
|
git-lfs clone https://huggingface.co/someone13574/mixtral-8x7b-32kseqlen
|
|
```
|
|
|
|
After that's done, combine the files:
|
|
```
|
|
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` convert and save the weights as NumPy arrays so
|
|
MLX can read them:
|
|
|
|
```
|
|
python convert.py --model_path mixtral-8x7b-32kseqlen/
|
|
```
|
|
|
|
The conversion script will save the converted weights in the same location.
|
|
|
|
After that's done, if you want to clean some stuff up:
|
|
|
|
```
|
|
rm mixtral-8x7b-32kseqlen/*.pth*
|
|
```
|
|
|
|
### Generate
|
|
|
|
As easy as:
|
|
|
|
```
|
|
python mixtral.py --model_path mixtral-8x7b-32kseqlen/
|
|
```
|
|
|
|
[^mixtral]: Refer to Mistral's [blog post](https://mistral.ai/news/mixtral-of-experts/) for more details.
|