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GLMap: Profiling genomic language models as individuals in a population (placeholder release; full v1.0.0 forthcoming with the paper)
Placeholder release (v0.0.1). The full v1.0.0 will be released together with the accompanying paper.
GLMap (Genomic Language Model Map) is a training-free, architecture-agnostic framework for representing and comparing genomic language models (GLMs) by their likelihood responses over a fixed panel of DNA sequences. Applied to 123 publicly available GLMs scored on a panel of 10,000 DNA probes, GLMap places autoregressive and masked-language models in a common space, yields model distances stable to the choice of probes, and predicts downstream task performance with Spearman ρ ≈ 0.7.
This 0.0.1 release reserves the glmap name on PyPI. The full library —
including model loaders, scoring code, panel construction, and the
clip + double-center matrix pipeline — is in active preparation:
Track progress at https://github.com/ai4nucleome/GLMap.
If you wish to cite GLMap before the v1.0.0 release, please cite the preprint:
Hou, Y., Long, W., Su, H., Feng, J., Zhang, Y.
Profiling genomic language models as individuals in a population.
(In submission, 2026.)
Apache-2.0.
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.0.1 | 23 May 2026 | 0 |
This describes our collected data; it is not a security assessment.
Observed since 21 Oct, 09:48 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:6358c49a2d4da2decef4fe361d2e0415755cb090a181a2d80ed1e91287238fcc