evo2

name	evo2
description	Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring regulatory or coding regions across species.
license	Apache-2.0
origin	openai4s
category	biomodels
requirements	["gpu"]
capabilities	{"network":{"mode":"raw_required","domains":[]}}
metadata	{"display-name":"Evo 2","third_party":[{"kind":"weights","name":"Evo 2","provider":"Arc Institute","license":"Apache-2.0","terms_url":"https://github.com/ArcInstitute/evo2/blob/main/LICENSE"}]}

Evo 2 — DNA Language Model

Prerequisites

Requirement	Minimum	Recommended
Python	3.11	3.12 (<3.13)
CUDA	12.1+	12.4+
GPU VRAM	24 GB (7B bf16)	80 GB (40B)
RAM	32 GB	128 GB

How to run

Installation

pip install evo2
# Weights pulled from Hugging Face on first model load.

Loading and scoring

from evo2 import Evo2

model = Evo2("evo2_7b")        # or "evo2_40b" — see model table
seqs = ["ATCG" * 50, "GGGCTTAA" * 25]
ll = model.score_sequences(seqs)   # → list[float], mean per-token log-likelihood
print(ll)

Generation

out = model.generate(
    prompt_seqs=["ATGAAAGCT"],
    n_tokens=256,
    temperature=0.7,
)
print(out.sequences[0])

Models

Name	Params	Context	VRAM (bf16)	Notes
evo2_7b	7 B	1 M nt	~22 GB	Default; fits on a single 24 GB+ GPU
evo2_40b	40 B	1 M nt	~78 GB	H100 80 GB or multi-GPU
evo2_1b_base	1 B	8 K nt	~6 GB	FP8 path requires sm_89+ (H100)

Output format

score_sequences returns a list[float] (or ) of mean log-likelihoods, one per input sequence. More negative ⇒ less likely under the model. For variant effect, compute over a fixed window.