mistral

Mistral

An example of generating text with Mistral using MLX.

Mistral 7B is one of the top large language models in its size class. It is also fully open source with a permissive license¹.

Setup

Install the dependencies:

pip install -r requirements.txt

Next, download the model and tokenizer:

curl -O https://models.mistralcdn.com/mistral-7b-v0-1/mistral-7B-v0.1.tar
tar -xf mistral-7B-v0.1.tar

Then, convert the weights with:

python convert.py --torch-path <path_to_torch>

To generate a 4-bit quantized model, use -q. For a full list of options:

python convert.py --help

By default, the conversion script will make the directory mlx_model and save the converted weights.npz, tokenizer.model, and config.json there.

Tip

Alternatively, you can also download a few converted checkpoints from the MLX Community organization on Hugging Face and skip the conversion step.

Run

Once you've converted the weights to MLX format, you can generate text with the Mistral model:

python mistral.py --prompt "It is a truth universally acknowledged,"

Run python mistral.py --help for more details.

Footnotes

1. Refer to the blog post and github repository for more details. ↩