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Vaibhav Srivastav 4b7e11bd31 Add URLs to HF MLX-Community org. (#153 ) * up * Add ref to MLX org on the README. * nit: language. * Standardise org name.		2023-12-20 06:57:13 -08:00
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.gitignore	mistral	2023-12-05 11:02:52 -08:00
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mistral.py	mixtral runs a bit faster	2023-12-12 08:36:40 -08:00
README.md	Add URLs to HF MLX-Community org. (#153 )	2023-12-20 06:57:13 -08:00
requirements.txt	Add missing numpy dependency	2023-12-06 15:34:55 -08:00
test.py	mistral	2023-12-05 11:02:52 -08:00

README.md

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://files.mistral-7b-v0-1.mistral.ai/mistral-7B-v0.1.tar
tar -xf mistral-7B-v0.1.tar

Then, convert the weights with:

python convert.py

The conversion script will save the converted weights in the same location.

Tip

Alternatively, you can also download a few converted checkpoints from the the MLX Community organisation 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,"  --temp 0

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

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