ModernGENA base

ModernGENA is a DNA foundation model based on ModernBERT (a modernized BERT-style encoder architecture) adapted for genomic sequence modeling.
ModernGENA base is the 377M-parameter version introduced in the paper Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models.

How to load pre-trained model to fine-tune it on classification task: GENA_LM repository

Technical features

• ModernBERT-based encoder architecture
• RoPE positional embeddings
• hybrid local/global attention
• pre-norm transformer blocks
• GeGLU feed-forward layers
• end-to-end unpadding
• FlashAttention-based efficient inference on compatible hardware
• torch.compile support

Model strengths

• strong efficiency-quality trade-off
• higher inference throughput with FlashAttention-based implementations
• competitive downstream performance on the Nucleotide Transformer benchmark
• intended to support long genomic contexts

This makes it a practical baseline for genomic modeling experiments and future architectural comparisons.

Tokenization

ModernGENA uses the 32k BPE vocabulary (AIRI-Institute/gena-lm-bert-base-t2t) introduced in GENA-LM, built over the DNA alphabet symbols A/T/G/C/N, with special tokens [CLS], [SEP], [PAD], [UNK], and [MASK].

Pretraining corpus

• 443 vertebrate genome assemblies
• 353,574,093,776 bp total
• Includes both forward strand and reverse complement sequences
• Excludes sequences containing ambiguous symbols other than A/C/G/T

To reduce overrepresentation of simple repeats and enrich biologically informative regions, training intervals were sampled around transcription start sites:

• window: [-16 kbp, +8 kbp] around each unique TSS
• overlapping intervals merged with BEDTools
• both strands included for each resulting region

Load pretrained model

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("AIRI-Institute/gena-lm-bert-base-t2t")
model = AutoModel.from_pretrained("AIRI-Institute/moderngena-base", trust_remote_code=True, attn_implementation="flash_attention_2")

Evaluation

For evaluation results, see our paper:

Citation