README.md

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

# Monarch Mixer-BERT

An 80M checkpoint of M2-BERT, pretrained with sequence length 2048.
**This is a BERT-style model that has not been fine-tuned. We recommend fine-tuning it for specific use cases before using it.**

Check out the paper [Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture](https://arxiv.org/abs/2310.12109) and our [blog post]() on retrieval for more on how we trained this model for long sequence.

This model was trained by Jon Saad-Falcon, Dan Fu, and Simran Arora.

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

## How to use

You can load this model using Hugging Face `AutoModel`:
```python
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained(
  "togethercomputer/m2-bert-80M-2k-retrieval",
  trust_remote_code=True
)
```

You should expect to see a large error message about unused parameters for FlashFFTConv.
If you'd like to load the model with FlashFFTConv, you can check out our [GitHub](https://github.com/HazyResearch/m2/tree/main).

## Acknowledgments

Alycia Lee helped with AutoModel support.

## Citation

If you use this model, or otherwise found our work valuable, you can cite us as follows:
```
@inproceedings{fu2023monarch,
  title={Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture},
  author={Fu, Daniel Y and Arora, Simran and Grogan, Jessica and Johnson, Isys and Eyuboglu, Sabri and Thomas, Armin W and Spector, Benjamin and Poli, Michael and Rudra, Atri and R{\'e}, Christopher},
  booktitle={Advances in Neural Information Processing Systems},
  year={2023}
}
```