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

Then call the wait_for_notification brain-tool. When the compute_done notification arrives, act on its payload:

save_artifacts(payload["featured_files"])   # paths under hpc/<job_id>/

For the full result dict (output_files, remote_workdir, …), re-enter the kernel: c.attach_job(job_id).result() then c.close(). See the remote-compute-ssh / remote-compute-modal skill for the orchestration 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 |

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