# evo-design/evo

Biological foundation modeling from molecular to genome scale

Repository: https://github.com/evo-design/evo
Canonical: https://ross.abutalabs.com/products/evo-design-evo
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-03-20T20:58:37+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 73, release rhythm 71, longevity 66
- inputs: {"age_days": 928, "days_push": 166, "days_rel": 198, "gap_med": 16.0, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1560, forks 185 (observed 2026-08-28T04:05:03.807380+00:00)

## What it is
Evo is a 7-billion-parameter DNA foundation model built on the StripedHyena architecture, trained on ~300 billion tokens of prokaryotic whole-genome data for single-nucleotide resolution sequence modeling and generation. This repository provides the Python code and pretrained checkpoints (8k and 131k context, plus CRISPR and transposon finetunes) for inference and genome-scale sequence design.

## Use cases
- generate synthetic dna sequences with a genomic language model
- model long dna contexts at single-nucleotide resolution
- design crispr-cas systems with a pretrained model
- generate functional de novo genes
- finetune a genome-scale model on molecular biology tasks
- explore the opengenome dataset for dna pretraining

## When to choose
- you need long-context dna sequence modeling or generation in python
- you want pretrained genomic foundation model checkpoints including crispr or transposon variants
- you are doing research in generative genomics or synthetic dna design

## When to avoid
- you need a general-purpose protein or rna model rather than dna
- you lack gpu resources for 7b-parameter inference
- you want a production-ready bioinformatics pipeline rather than a research codebase

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-inference, data-generation
- domain: artificial-intelligence, machine-learning, bioinformatics, large-language-models
- platform: python
- tags: dna-foundation-model, genomics, stripedhyena, long-context, sequence-modeling, biological-design, huggingface, gpu, linux

## Member repositories
- evo-design/evo (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:03.807380+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:29:47.022573+00:00, confidence not recorded.
  - readme: https://github.com/evo-design/evo (fetched 2026-08-28T04:05:03.807380+00:00, sha bb91ada6625a)
- Data as of 2026-08-30T08:39:29.467469+00:00.