gLM2_150M

gLM2 is a mixed-modality genomic language model, trained on the OMG Dataset. The model encodes a genomic scaffold with both both amino-acid and DNA tokens.

gLM2 is trained at two scales: 150M and 650M parameters (available at tattabio/gLM2_650M).

See https://github.com/TattaBio/gLM2 for inference scripts.

Model Description

gLM2 is a transformer encoder trained with the masked language modeling objective.
It encodes a genomic contig as a sequence of protein coding sequences (CDS) and DNA inter-genic sequences (IGS).
CDS elements are tokenized using per-amino acid tokens, and IGS elements are tokenized using per-nucleotide tokens.

• To encode the genomic strand, we prepended each genomic element with a special token, either <+> or <-> to indicate the positive and negative strands.
• To avoid collision between amino acid and nucleotide tokens, the tokenizer expects all amino acids to be uppercase, and all nucleotides to be lowercase.

UPDATE(09/2024): We updated the model with longer context length (4096 tokens vs. 2048 tokens) and per-nucleotide IGS tokenization instead of BPE.

Getting Started

import torch
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained('tattabio/gLM2_150M', torch_dtype=torch.bfloat16, trust_remote_code=True).cuda()
tokenizer = AutoTokenizer.from_pretrained('tattabio/gLM2_150M', trust_remote_code=True)

# A contig with two proteins and an inter-genic sequence.
# NOTE: Nucleotides should always be lowercase, and prepended with `<+>`.
sequence = "<+>MALTKVEKRNRIKRRVRGKISGTQASPRLSVYKSNK<+>aatttaaggaa<->MLGIDNIERVKPGGLELVDRLVAVNRVTKVTKGGRAFGFSAIVVVGNED"

# Tokenize the sequence.
encodings = tokenizer([sequence], return_tensors='pt')

# Extract embeddings.
with torch.no_grad():
    embeddings = model(encodings.input_ids.cuda(), output_hidden_states=True).last_hidden_state

Training Data

gLM2 is trained on the OMG dataset. To improve the dataset balance and remove near-duplicate examples, the data is tokenized and pruned by applying Semantic Deduplication SemDedup.
We use an embedding distance threshold of 2e-3, resulting in 49% of the dataset being pruned.

Training Details

• Pretraining tokens: 315B
• Context length: 4096
• Masking rate: 30%
• Learning rate: 1e-3
• Optimizer: AdamW (betas = (0.9, 0.95))
• Mixed precision training: bfloat16
• Weight decay: 0.1

Citation

BioRxiv: https://www.biorxiv.org/content/10.1101/2024.08.14.607850

BibTeX:

    author = {Cornman, Andre and West-Roberts, Jacob and Camargo, Antonio Pedro and Roux, Simon and Beracochea, Martin and Mirdita, Milot and Ovchinnikov, Sergey and Hwang, Yunha},
    title = {The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling},
    elocation-id = {2024.08.14.607850},
    year = {2024},
    doi = {10.1101/2024.08.14.607850},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2024/08/17/2024.08.14.607850},
    eprint = {https://www.biorxiv.org/content/early/2024/08/17/2024.08.14.607850.full.pdf},
    journal = {bioRxiv}
}