metadata

license: apache-2.0
library_name: mlx-llm
language:
  - en
tags:
  - mlx
  - exbert
datasets:
  - bookcorpus
  - wikipedia

BERT base model (uncased) - MLX

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.

Model description

Please, refer to the original model card for more details on bert-base-uncased.

Use it with mlx-llm

Install mlx-llm from GitHub.

git clone https://github.com/riccardomusmeci/mlx-llm
cd mlx-llm
pip install .

Run

from mlx_llm.model import create_model
from transformers import BertTokenizer
import mlx.core as mx

model = create_model("bert-base-uncased") # it will download weights from this repository
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

batch = ["This is an example of BERT working on MLX."]
tokens = tokenizer(batch, return_tensors="np", padding=True)
tokens = {key: mx.array(v) for key, v in tokens.items()}

output, pooled = model(**tokens)