evo2
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.
Profile →npx skills add xuzhougeng/wisp-science --skill evo2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```bash pip install evo2 # Weights pulled from Hugging Face on first model load. ``` ### Loading and scoring ```python 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 ```python 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
- Prerequisites
- How to run
- Installation
- Loading and scoring
- Generation
- Models
- Output format
- Decision tree
- Remote compute
- Typical performance
- Troubleshooting
pip install evo2 Weights pulled from Hugging Face on first model load.
What does the evo2 skill do?
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.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill evo2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From xuzhougeng/wisp-science, a repository with 895 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.