---
license: apache-2.0
language:
- en
pipeline_tag: fill-mask
---

# Monarch Mixer-BERT

The 110M checkpoint for M2-BERT-base from the paper [Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture](https://arxiv.org/abs/2310.12109).

Check out our [GitHub](https://github.com/HazyResearch/m2/tree/main) for instructions on how to download and fine-tune it!

## How to use

Using AutoModel:
```python
from transformers import AutoModelForMaskedLM
mlm = AutoModelForMaskedLM.from_pretrained('alycialee/m2-bert-110M', trust_remote_code=True)
```

You can use this model with a pipeline for masked language modeling:
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='alycialee/m2-bert-110M', trust_remote_code=True)
unmasker("Every morning, I enjoy a cup of [MASK] to start my day.")
```

### Remote Code

This model requires `trust_remote_code=True` to be passed to the `from_pretrained` method. This is because we use custom PyTorch code (see our GitHub). You should consider passing a `revision` argument that specifies the exact git commit of the code, for example:

```python
mlm = AutoModelForMaskedLM.from_pretrained(
   'alycialee/m2-bert-110M',
   trust_remote_code=True,
   revision='eee02a4',
)
```

### Configuration
Note `use_flash_mm` is false by default. Using FlashMM is currently not supported.
Using `hyena_training_additions` is turned off.