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Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi.
Passed 18 of 19 test cases with the skill, and 8 of 19 without it.
and 14 more in the full report
12 fixed10 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
When evaluating mouse genomic loci with the Borzoi mouse prediction head enabled, how many total mouse target output tracks does the model output?
| Checked | Without skill | With skill |
|---|---|---|
| The response specifies 2,608 tracks for mouse predictions. | Missed | Passed |
The instructions your model reads when the skill loads.
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.11 |
| CUDA | 12.1+ | 12.4+ |
| GPU VRAM | 16 GB | 24 GB+ |
from borzoi_pytorch import Borzoi
model = Borzoi.from_pretrained("johahi/borzoi-replicate-0").cuda().eval()
# input: (batch, 4, 524288) one-hot DNA → output: (batch, tracks, 6144) bins
Borzoi consumes ~524 kb one-hot windows and emits binned predictions across
7,611 human tracks (the separate 2,608-track mouse head is off by default;
enable via enable_mouse_head=True and select with
forward(..., is_human=False)). For variant scoring, run ref/alt windows
centred on the variant and compare per-track output.
(B, T, L) tensor — T tracks × L 32-bp bins. Track metadata (assay,
biosample) is in borzoi_pytorch.pytorch_borzoi_model.TRACKS_DF (or model.tracks_df when using the AnnotatedBorzoi subclass) — the base Borzoi model has no targets attribute.
Needs ≥24 GB VRAM and either pre-cached HF weights or egress to
huggingface.co. Use a selected and probed ssh:<alias> context and load
remote-compute-ssh. Confirm borzoi-pytorch and the cache location, then
submit a self-contained runner with run_in_context:
{
test cases passed, vs 8 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.