--- license: apache-2.0 language: - en pipeline_tag: fill-mask inference: false --- # Monarch Mixer-BERT The 80M checkpoint for M2-BERT-128 from the paper [Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT](https://arxiv.org/abs/2402.07440). 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("jonsaadfalcon/M2-BERT-128-Retrieval-Encoder-V1", trust_remote_code=True) ``` This model uses the Hugging Face `bert-base-uncased tokenizer`: ``` from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') ``` ## How to use This model generates embeddings for retrieval. The embeddings have a dimensionality of 768: ``` from transformers import AutoTokenizer, AutoModelForMaskedLM max_seq_length = 128 testing_string = "Every morning, I make a cup of coffee to start my day." model = AutoModelForMaskedLM.from_pretrained("jonsaadfalcon/M2-BERT-128-Retrieval-Encoder-V1", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", model_max_length=max_seq_length) input_ids = tokenizer([testing_string], return_tensors="pt", padding="max_length", return_token_type_ids=False, truncation=True, max_length=max_seq_length) outputs = model(**input_ids) embeddings = outputs['sentence_embedding'] ``` ### 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( "jonsaadfalcon/M2-BERT-128-Retrieval-Encoder-V1", trust_remote_code=True, ) ``` ### Configuration Note `use_flash_mm` is false by default. Using FlashMM is currently not supported.