| name | evo2 |
| description | Evo 2 — long-context DNA language model for genome modeling and design across all domains of life (Arc Institute; StripedHyena 2 / OpenGenome2). Use when: (1) Autoregressive DNA likelihoods or embeddings at up to ~1 Mb context, (2) Sequence generation / design, (3) Contrasting generative gLMs with Nucleotide Transformer or gLM2. Upstream: https://github.com/ArcInstitute/evo2. Predecessor: Evo (Science 2024). Route via genome-language-model. Heavy GPU requirements.
|
| license | MIT |
| category | analysis-tools |
| tags | ["Evo2","Evo","gLM","DNA","generative","OpenGenome2","Arc-Institute"] |
| upstream | https://github.com/ArcInstitute/evo2 |
| stage | function |
Evo 2
Upstream: ArcInstitute/evo2 ·
Dataset: https://huggingface.co/datasets/arcinstitute/opengenome2
Citation
Brixi, G. et al. Genome modelling and design across all domains of life with
Evo 2. Nature https://doi.org/10.1038/s41586-026-10176-5 (2026).
Nguyen, E. et al. Sequence modeling and design from molecular to genome scale
with Evo. Science 386, eado9336 (2024).
https://doi.org/10.1126/science.ado9336
See also docs/references.md.
Analytical thinking
Evo 2 is a generative / long-context DNA LM (single-nucleotide resolution,
contexts up to ~1M bp) pretrained on OpenGenome2 (~8.8T tokens, all domains).
Checkpoints include evo2_7b (widest hardware reach) through evo2_40b (FP8 /
Hopper-class for full accuracy per upstream).
| vs | Prefer Evo 2 when |
|---|
nucleotide-transformer | Generation / long AR likelihoods across domains |
omg / gLM2 | Pure DNA generative modeling vs mixed AA+DNA metagenomic LM |
| Classical assemblers | Design/scoring sequences — not contig assembly |
Intermediate-layer embeddings often work better than final layer (per paper).
Validate outputs after hardware/config changes (upstream tests).
How to run
pip install evo2
python - <<'PY'
from evo2 import Evo2
import torch
evo2_model = Evo2('evo2_7b')
sequence = 'ACGT'
input_ids = torch.tensor(
evo2_model.tokenizer.tokenize(sequence), dtype=torch.int
).unsqueeze(0).to('cuda:0')
PY
Also: NVIDIA hosted API / NIM options documented upstream.
Decision tree
Long DNA LM / design?
├─ Generative + all-domains pretrain → evo2
├─ Multi-species tracks / NTv3 representation → nucleotide-transformer
├─ Efficient / long-range representation → dnabert2 / caduceus
├─ Metagenomic gLM2 / OMG corpus → omg
└─ Classical search → homology-search
Related skills
genome-language-model · nucleotide-transformer · dnabert2 · caduceus ·
omg · metagenomics-llm · lucaphylo · tool-selection
