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These checkpoints are derivative works of Meta's UMA and AllScAIP models and are provided under the FAIR Chemistry License (see LICENSE), including the FAIR Chemistry Acceptable Use Policy. Publications that use them must acknowledge the use of the Meta materials.
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UniHyena baseline checkpoints
Fine-tuned UMA and AllScAIP checkpoints used as baselines in the UniHyena project, which benchmarks a new machine-learning interatomic potential against them on OC20 S2EF, MatPES, MD22, OCELOT and QMOF.
Contents
One folder per run: <benchmark>/<benchmark>-<target>-<model>/
| File | What |
|---|---|
inference_ckpt.pt |
final fine-tuned checkpoint (fairchem inference format, no optimizer state) |
config/ |
the fairchem fine-tuning config the run used |
results/ |
validation / test metrics, run summary, split metadata, training log |
Load one with fairchem (commit 8dacc96ae56a7fa5aac158ba0559b039d1f8b727):
from fairchem.core import FAIRChemCalculator
from fairchem.core.units.mlip_unit import load_predict_unit
predictor = load_predict_unit("md22/md22-DHA-uma/inference_ckpt.pt")
calc = FAIRChemCalculator(predictor, task_name="omol") # MD22 runs use the omol task
Base models
- UMA-S 1.2.1 (
uma-s-1p2p1, fromfacebook/UMA): Wood et al., UMA: A Family of Universal Models for Atoms, arXiv 2506.23971. - AllScAIP-md conserving (
allscaip-md-conserving-all-omol, fromfacebook/OMol25): Qu et al., A recipe for scalable attention-based MLIPs, arXiv 2603.06567.
License
These checkpoints are derivative works of Meta's UMA and AllScAIP models and are distributed under
the FAIR Chemistry License (included as LICENSE), subject to the FAIR Chemistry
Acceptable Use Policy it contains. By using them you agree to those terms. Publications that use
them must acknowledge the use of the Meta materials. The upstream models are not offered in
comprehensively sanctioned jurisdictions, China, Russia or Belarus; the same restriction applies here.