README.md

---
license: other
license_name: mixed-upstream-oss
license_link: https://github.com/ai4nucleome/GLMap
pretty_name: GLMap scoring containers
tags:
- genomics
- dna
- genomic-language-model
- apptainer
- singularity
- container
- bioinformatics
- reproducibility
viewer: false
---

# 🧬 🗺️ GLMap scoring containers

Prebuilt **Apptainer / Singularity** images that carry the GPU runtime
environments for **scoring all 123 genomic language models (gLMs)** profiled in
[**GLMap**](https://github.com/ai4nucleome/GLMap) — *Profiling genomic language
models as individuals in a population*.

The 123 models span runtime stacks that are **mutually incompatible** (Python
3.8–3.12, PyTorch 1.13–2.9, CUDA 11.7–12.4) and can never share one interpreter.
These four images package every environment so you can recompute the
likelihood responses **without setting up a single conda env**.

> Only need the **analysis** (precomputed scores, figures/tables)? You don't
> need these images at all — `pip install -e .` on the
> [GLMap repo](https://github.com/ai4nucleome/GLMap) gives a torch-free stack.

## What's here

Each image is **self-contained** (the shared CUDA 12.8 base is already inside;
download only the group(s) for the models you want to score):

| Image | Size | Envs | Model families |
|---|---|---|---|
| `bio-default.sif` | 17 GB | base / dnabert2 / megadna | NT, GENA-LM, ModernBERT, GROVER, Mistral-DNA, NTv3, … (most); DNABERT-2 / DNABERT-S; megaDNA |
| `bio-cu118.sif`   | 20 GB | caduceus / gf / hyena-dna | Caduceus; GenomeOcean; HyenaDNA |
| `bio-cu121.sif`   | 15 GB | PlantCAD | PlantCAD2 |
| `bio-evo.sif`     | 24 GB | evo / evo2 | Evo-1 / Evo-1.5; Evo-2 (7B) |

Each image holds its envs as isolated micromamba environments and dispatches to
the right one per model via the `GLMAP_ENV` variable.

## Download

```bash
# one image
hf download Tim419/GLMap-containers bio-default.sif --repo-type dataset --local-dir .

# or all four
hf download Tim419/GLMap-containers --repo-type dataset --local-dir .
```

## Run

Bind your [GLMap checkout](https://github.com/ai4nucleome/GLMap) at `/work`
(code, panel, audit, model weights) and pick the env with `GLMAP_ENV`:

```bash
GLMAP_ENV=caduceus apptainer run --nv \
    --bind "$PWD":/work --pwd /work bio-cu118.sif \
    scripts/score/scoring_worker.py --from-audit \
    --hf-ids kuleshov-group/caduceus-ph_seqlen-131k_d_model-256_n_layer-16
```

Or run the **full 123-model sweep** straight through the images:

```bash
python scripts/score/run_scoring_sweep.py \
    --backend container --image-dir <dir with the .sif files> --hf-cache "$HF_HOME"
```

- `--nv` exposes the host GPU.
- On compute nodes **without user namespaces**, use `singularity run --nv`
  (the same `.sif`) — the GLMap sweep takes `--container-runtime singularity`.
- **HyenaDNA / megaDNA** also need their loader code on the bound checkout: it
  is in the GLMap repo after `bash models/setup_external_models.sh` (HyenaDNA's
  is vendored; megaDNA's weight auto-downloads from the HF Hub).

See [`container/README.md`](https://github.com/ai4nucleome/GLMap/blob/main/container/README.md)
and [`models/env_routing.md`](https://github.com/ai4nucleome/GLMap/blob/main/models/env_routing.md)
for the full model → image/env routing.

## License

These images bundle many third-party open-source runtimes (PyTorch,
Transformers, mamba-ssm, flash-attn, evo2, …) and each model family's loader
code — **each remains under its own upstream license**. The GLMap glue code is
Apache-2.0. Individual model **weights** are downloaded separately and follow
their own licenses. Consult each upstream project before redistribution or
commercial use.

## Citation

```bibtex
@article{hou2026glmap,
  title   = {Profiling genomic language models as individuals in a population},
  author  = {Hou, Yusen and Long, Weicai and Su, Houcheng and Feng, Junning and Zhang, Yanlin},
  journal = {In submission},
  year    = {2026}
}
```

Project: <https://github.com/ai4nucleome/GLMap> · Panel dataset:
[`Tim419/GLMap-panels`](https://huggingface.co/datasets/Tim419/GLMap-panels)