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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: fill-mask |
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inference: false |
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--- |
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# Monarch Mixer-BERT |
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An 80M checkpoint of M2-BERT, pretrained with sequence length 2048. |
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**This is a BERT-style model that has not been fine-tuned. We recommend fine-tuning it for specific use cases before using it.** |
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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. |
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This model was trained by Jon Saad-Falcon, Dan Fu, and Simran Arora. |
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Check out our [GitHub](https://github.com/HazyResearch/m2/tree/main) for instructions on how to download and fine-tune it! |
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## How to use |
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You can load this model using Hugging Face `AutoModel`: |
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```python |
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from transformers import AutoModelForMaskedLM |
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model = AutoModelForMaskedLM.from_pretrained( |
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"togethercomputer/m2-bert-80M-2k-retrieval", |
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trust_remote_code=True |
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) |
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``` |
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You should expect to see a large error message about unused parameters for FlashFFTConv. |
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If you'd like to load the model with FlashFFTConv, you can check out our [GitHub](https://github.com/HazyResearch/m2/tree/main). |
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## Acknowledgments |
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Alycia Lee helped with AutoModel support. |
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## Citation |
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If you use this model, or otherwise found our work valuable, you can cite us as follows: |
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``` |
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@inproceedings{fu2023monarch, |
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title={Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture}, |
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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}, |
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booktitle={Advances in Neural Information Processing Systems}, |
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year={2023} |
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} |
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``` |
