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Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model.
Passed 20 of 20 test cases with the skill, and 6 of 20 without it.
and 15 more in the full report
15 fixed7 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need a fast, low-memory model footprint (~6 GB VRAM) for 5 KB genomic fragments on a consumer GPU. What model variant name should I request?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies evo2_1b_base for low VRAM usage | Missed | Passed |
The instructions your model reads when the skill loads.
| 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 |
pip install evo2
# Weights pulled from Hugging Face on first model load.
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)
out = model.generate(
prompt_seqs=["ATGAAAGCT"],
n_tokens=256,
temperature=0.7,
)
print(out.sequences[0])
test cases passed, vs 6 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.