darlednik/LDARNet-110M

Pretrained LDARNet (~2M params) with learnable DNA tokenization (dynamic chunking + BiMamba-2).

The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes. Compared to other approaches, our models do not only integrate information from single reference genomes, but leverage DNA sequences from over 3,200 diverse human…

The plant DNA large language models (LLMs) contain a series of foundation models based on different model architectures, which are pre-trained on various plant reference genomes. All the models have a comparable model size between 90 MB and 150 MB, BPE tokenizer is used for tokenization and 8000…

# 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…

Foundation models for genomics and transcriptomics pretrained on 3,000+ human genomes and 850+ diverse species, enabling chromatin accessibility prediction, splice site detection, and promoter classification across multiple model scales (InstaDeep, NVIDIA & TUM, Nature Methods 2023)