evo2

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# 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=["score_evo2.py"],
    command="python3 score_evo2.py",   # env selection is host-specific — see compute_details for your provider
    outputs=["scores.json"],
    run_timeout_s=1800,
)
# the Job repr just printed itself (id, notes) — cell ends here; the kernel never waits on the job
```

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

```python
save_artifacts(payload["output_files"])   # paths under hpc/<job_id>/
```

For the full record (`files`, `stdout_tail`, `notes`, …), re-enter the
kernel: `res = c.attach_job(job_id).result()` then `c.close(intent=...)`. 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     |

From flyme2bluemoon/skills, Apache-2.0.

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