evo2

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