/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
category: biomodels
requirements: [gpu]
metadata:
  display-name: Evo 2
  # github.com/ArcInstitute/evo2/blob/main/LICENSE: Apache-2.0 boilerplate.
  # HuggingFace model cards `arcinstitute/evo2_{40b_base,20b}` declare
  # `license: apache-2.0`. verified 2026-06-30
  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 `np.ndarray`) of mean log-likelihoods, one per input sequence. More negative ⇒ less likely under the model. For variant effect, compute `Δll = ll_alt - ll_ref` over a fixed window.

`generate` returns a `GenerationOutput` with `.sequences` (list[str]), `.logits` (list[Tensor]), and `.logprobs_mean` (list[float]) — always populated, no flag required.

Decision tree

Need a DNA model?
│
├─ Per-base/per-sequence likelihood, generation → Evo 2 ✓
├─ Predict experimental tracks (expression, accessibility) → borzoi
└─ Protein, not DNA → fair-esm2 / esmfold2

Remote compute

7B/40B inference is GPU-bound (≥24 GB / 80 GB VRAM). Read `compute_details({provider, mode:'read'})` for an environment with `evo2` + `flash-attn` and a pre-cached HF weight mount, then submit:

c = host.compute.create(provider)
job = c.submitJob(
    intent="Evo2-7B score 200bp variant window — 1×GPU, ~2 min",
    inputs=[{"src": "score_evo2.py", "dstFilename": "score_evo2.py"}],
    command="python3 score_evo2.py",   # env selection is host-specific — see compute_details for your provider
    outputs=["scores.json"],
    timeoutSeconds=1800,
)
print(job.job_id)   # cell ends here — kernel never blocks on compute

Retain the exact returned `job_id`. Query that saved ID with the non-blocking `c.attachJob(job_id).status()` or `.result()` when its state or result is relevant; do not scan Job history. A final `.result()` read reports whether its follow-up was `suppressed` or had already been `committed`; otherwise the app starts the later analysis turn for an unread final result. See the `remote-compute-ssh` skill for details.

Inside `score_evo2.py`, point `HF_HOME` at the provider's weight-cache mount (path is in `compute_details`) and set `HF_HUB_OFFLINE=1` so the loader doesn't try to write `refs/` into a read-only mount. Weight footprint: ~15 GB (7B), ~80 GB (40B).

Typical performance

| Task | 7B on H100 | Notes | | ---------------------------- | ---------- | --------------------------- | | Model load (cached) | ~5-7 min | First call hydrates weights | | `score_sequences`, 200×200bp | ~10-20 s | After load | | `generate`, 1×512 nt | ~15 s | |

Troubleshooting

| Symptom | Cause | Fix | | ------------------------------------- | ---------------------------- | ------------------------------------------- | | `Transformer Engine not installed` | No FP8 — falls back to bf16 | Informational only on non-H100; ignore | | OOM on load | 40B on <80 GB GPU | Use `evo2_7b` or shard with `device_map` | | HF tries to write `refs/main` | `HF_HOME` points at RO mount | Set `HF_HUB_OFFLINE=1` | | `dtype mismatch` in `score_sequences` | Passing tensors not strings | Pass `list[str]`; the API tokenises for you |

---

**Next**: pair with `borzoi` to predict track-level effects of the same variants.