MLX Transformers

MLX Transformers is a library that provides model implementation in MLX. It uses a similar model interface as HuggingFace Transformers and provides a way to load and use models in Apple Silicon devices. Implemented models have the same modules

MLX transformers is currently only available for infernce on Apple Silicon devices. Training support will be added in the future.

Installation

This library is available on PyPI and can be installed using pip:

pip install mlx-transformers

Quick Tour

A list of the available models can be found in the mlx_transformers.models module and are also listed in the section below. The following example demonstrates how to load a model and use it for inference:

• First you need to download and convert the model checkpoint to MLX format To do this from huggingface

  from transformers import BertModel
  from mlx_transformers.models.utils import convert
  
  model_name_or_path = "bert-base-uncased"
  mlx_checkpoint = "bert-base-uncased.npz"
  
  convert("bert-base-uncased", "bert-base-uncased.npz", BertModel)

  This will download the model checkpoint from huggingface and convert it to MLX format.

• Now you can load the model using MLX transformers in few lines of code

  from transformers import BertConfig, BertTokenizer
  from mlx_transformers.models import BertModel as MLXBertModel
  
  tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
  config = BertConfig.from_pretrained("bert-base-uncased")
  model = MLXBertModel(config)
  
  sample_input = "Hello, world!"
  inputs = tokenizer(sample_input, return_tensors="np")
  outputs = model(**inputs)

Available Models

The following models have been ported to MLX Transformers from Huggingface for inference:

1. Bert
2. Roberta
3. XLMRoberta
4. M2M100

Examples

Coming soon...

Benchmarks

Coming soon...

Contributions

Contributions to MLX transformers are welcome. See the contributing documentation for instructions on setting up a development environment.