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Evo2 Gene Modelling

Published at
2 days ago
Main Article
https://github.com/ArcInstitute/evo2

Evo 2: Genome modeling and design across all domains of life

Evo 2

Evo 2 is a state of the art DNA language model for long context modeling and design. Evo 2 models DNA sequences at single-nucleotide resolution at up to 1 million base pair context length using the StripedHyena 2 architecture. Evo 2 was pretrained using Savanna. Evo 2 was trained autoregressively on OpenGenome2, a dataset containing 8.8 trillion tokens from all domains of life.

We describe Evo 2 in the preprint: "Genome modeling and design across all domains of life with Evo 2".

Contents

Setup

Requirements

Evo 2 is based on StripedHyena 2 which requires python>=3.11. Evo 2 uses Transformer Engine FP8 for some layers which requires an H100 (or other GPU with compute capability ≥8.9). We are actively investigating ways to avoid this requirement.

You can also run Evo 2 without any installation using the Nvidia Hosted API.

To host your own instance for running Evo 2, we recommend using NVIDIA NIM. This will allow you to self-host an instance with the same API as the Nvidia hosted API. See the NVIDIA NIM section for more information.

Installation

Please clone and install from GitHub. We recommend using a new conda environment with python>=3.11.

git clone --recurse-submodules [email protected]:ArcInstitute/evo2.git
cd evo2
pip install .

If this did not work for whatever reason, you can also install from Vortex and follow the instructions there. PyPi support coming soon!

You can check that the installation was correct by running a test.

python ./test/test_evo2.py --model_name evo2_7b

Checkpoints

We provide the following model checkpoints, hosted on HuggingFace:

Checkpoint Name Description
evo2_40b A model pretrained with 1 million context obtained through context extension of evo2_40b_base.
evo2_7b A model pretrained with 1 million context obtained through context extension of evo2_7b_base.
evo2_40b_base A model pretrained with 8192 context length.
evo2_7b_base A model pretrained with 8192 context length.
evo2_1b_base A smaller model pretrained with 8192 context length.

To use Evo 2 40B, you will need multiple GPUs. Vortex automatically handles device placement, splitting the model across available cuda devices.

Usage

Below are simple examples of how to download Evo 2 and use it locally using Python.

Forward

Evo 2 can be used to score the likelihoods across a DNA sequence.

import torch
from evo2 import Evo2

evo2_model = Evo2('evo2_7b')

sequence = 'ACGT'
input_ids = torch.tensor(
    evo2_model.tokenizer.tokenize(sequence),
    dtype=torch.int,
).unsqueeze(0).to('cuda:0')

outputs, _ = evo2_model(input_ids)
logits = outputs[0]

print('Logits: ', logits)
print('Shape (batch, length, vocab): ', logits.shape)

Embeddings

Evo 2 embeddings can be saved for use downstream.

import torch
from evo2 import Evo2

evo2_model = Evo2('evo2_7b')

sequence = 'ACGT'
input_ids = torch.tensor(
    evo2_model.tokenizer.tokenize(sequence),
    dtype=torch.int,
).unsqueeze(0).to('cuda:0')

layer_name = 'blocks.28.mlp.l3'

outputs, embeddings = evo2_model(input_ids, return_embeddings=True, layer_names=[layer_name])

print('Embeddings shape: ', embeddings[layer_name].shape)

Generation

Evo 2 can generate DNA sequences based on prompts.

from evo2 import Evo2

evo2_model = Evo2('evo2_7b')

output = evo2_model.generate(prompt_seqs=["ACGT"], n_tokens=400, temperature=1.0, top_k=4)

print(output.sequences[0])

Notebooks

We provide an example notebook of zero-shot BRCA1 variant effect prediction. This example includes a walkthrough of:

  • Performing zero-shot BRCA1 variant effect predictions using Evo 2
  • Reference vs alternative allele normalization

NVIDIA NIM for Evo 2

Evo 2 is available on NVIDIA NIM.

The quickstart guides users through running Evo 2 on the NVIDIA NIM using a python or shell client after starting NIM. An example python client script is shown below. Note this is the same way you would interact with the Nvidia hosted API.

#!/usr/bin/env python3
import requests
import os
import json
from pathlib import Path

r = requests.post(
    url="http://localhost:8000/biology/arc/evo2/generate",
    json={
        "sequence": "ACTGACTGACTGACTG",
        "num_tokens": 8,
        "top_k": 1,
        "enable_sampled_probs": True,
    },
)
print(r, "Saving to output.json:\n", r.text[:200], "...")
Path("output.json").write_text(r.text)

Very long sequences

We are actively working on optimizing performance for long sequence processing. Vortex can currently compute over very long sequences via teacher prompting. However please note that forward pass on long sequences may currently be slow.

Dataset

The OpenGenome2 dataset used for pretraining Evo2 is available on HuggingFace . Data is available either as raw fastas or as JSONL files which include preprocessing and data augmentation.

Training Code

Evo 2 was trained using Savanna, an open source framework for training alternative architectures.

Citation

If you find these models useful for your research, please cite the relevant papers

@article{brixi2025genome,
  title = {Genome modeling and design across all domains of life with Evo 2},
  author = {Brixi, Garyk and Durrant, Matthew G. and Ku, Jerome and Poli, Michael and Brockman, Greg and Chang, Daniel and Gonzalez, Gabriel A. and King, Samuel H. and Li, David B. and Merchant, Aditi T. and Naghipourfar, Mohsen and Nguyen, Eric and Ricci-Tam, Chiara and Romero, David W. and Sun, Gwanggyu and Taghibakshi, Ali and Vorontsov, Anton and Yang, Brandon and Deng, Myra and Gorton, Liv and Nguyen, Nam and Wang, Nicholas K. and Adams, Etowah and Baccus, Stephen A. and Dillmann, Steven and Ermon, Stefano and Guo, Daniel and Ilango, Rajesh and Janik, Ken and Lu, Amy X. and Mehta, Reshma and Mofrad, Mohammad R.K. and Ng, Madelena Y. and Pannu, Jaspreet and Ré, Christopher and Schmok, Jonathan C. and St. John, John and Sullivan, Jeremy and Zhu, Kevin and Zynda, Greg and Balsam, Daniel and Collison, Patrick and Costa, Anthony B. and Hernandez-Boussard, Tina and Ho, Eric and Liu, Ming-Yu and McGrath, Thomas and Powell, Kimberly and Burke, Dave P. and Goodarzi, Hani and Hsu, Patrick D. and Hie, Brian L.},
  journal = {Arc Institute Manuscripts},
  year = {2025},
  url = {https://arcinstitute.org/manuscripts/Evo2}
}