Evo 2

// Overview

Evo 2 is a genome foundation model from the Arc Institute for modeling and designing DNA sequences at single-nucleotide resolution. It works with contexts of up to 1 million base pairs, which lets it read long stretches of a genome at once rather than short fragments.

The model was trained autoregressively on OpenGenome2, a dataset of 8.8 trillion tokens drawn from all domains of life, using the StripedHyena 2 architecture. It ships in several sizes, including 1B, 7B, 20B, and 40B parameter checkpoints, with variants that support 8K, 262K, or 1M base-pair context windows.

Evo 2 supports three main tasks: scoring sequences by computing their likelihoods, extracting intermediate-layer embeddings for downstream analysis, and generating new DNA sequences from a prompt. It is distributed as a Python package that runs on NVIDIA GPUs.

// What it does

• DNA language model that works at single-nucleotide resolution with up to 1 million base-pair context
• Multiple checkpoints from 1B to 40B parameters, with 8K, 262K, and 1M context variants
• Trained on OpenGenome2, a dataset of 8.8 trillion tokens spanning all domains of life
• Zero-shot variant effect prediction, demonstrated on BRCA1 scoring
• Sequence scoring, embedding extraction, and autoregressive DNA generation from a single API
• Example notebooks for BRCA1 scoring, generation, exon classification, and sparse-autoencoder analysis

// Getting started

Install Evo 2 on a Linux machine with an NVIDIA GPU, then load a model to score, embed, or generate DNA sequences from Python.

pip install flash-attn==2.8.0.post2 --no-build-isolation
pip install evo2

python -m evo2.test.test_evo2_generation --model_name evo2_7b

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])

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])

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Predict the effect of genetic variants without task-specific training, as shown with BRCA1 scoring
• Extract sequence embeddings to feed downstream classifiers, such as exon classification
• Generate candidate DNA sequences from a prompt for sequence design work
• Score long genomic regions by likelihood using the model's long-context window

// How Evo 2 compares

Evo 2 alongside other open-source genomics & single-cell tools AI/TLDR tracks, ranked by GitHub stars.