mlxbert

Update: this model is being merged into https://github.com/ml-explore/mlx-examples, all future work will take place there.

A BERT implementation in Apple's new MLX framework.

Dependency Installation

poetry install --no-root

If you don't want to do that, simply make sure you have the following dependencies installed:

mlx
transformers
numpy

Download and Convert

python convert.py \
    --bert-model bert-base-uncased
    --mlx-model weights/bert-base-uncased.npz

Run the Model

Right now, this is just a test to show tha the outputs from mlx and huggingface don't change all that much.

python model.py \
  --bert-model bert-base-uncased \
  --mlx-model weights/bert-base-uncased.npz

Which will show the following outputs:

MLX BERT:
[[[-0.17057164  0.08602728 -0.12471077 ... -0.09469379 -0.00275938
    0.28314582]
  [ 0.15222196 -0.48997563 -0.26665813 ... -0.19935863 -0.17162783
   -0.51360303]
  [ 0.9460105   0.1358298  -0.2945672  ...  0.00868467 -0.90271163
   -0.2785422 ]]]

They can be compared against the 🤗 implementation with:

python hf_model.py \
  --bert-model bert-base-uncased

Which will show:

 HF BERT:
[[[-0.17057131  0.08602707 -0.12471108 ... -0.09469365 -0.00275959
    0.28314728]
  [ 0.15222463 -0.48997375 -0.26665992 ... -0.19936043 -0.17162988
   -0.5136028 ]
  [ 0.946011    0.13582966 -0.29456618 ...  0.00868565 -0.90271175
   -0.27854213]]]

To do's

fix position encodings
bert large and cased variants loaded
example usage

jbarrow / mlxbert