Stanford SNAP

weights

open(ungated 4-layer and 33-layer checkpoints, fetched by the evaluation script or from Figshare)

data

documented-not-released(the paper describes the 36-million-cell Integrated Mega-scale Atlas corpus while the repository links processed paper datasets rather than the pretraining corpus)

code

partial(embedding and evaluation script only

license

CC-BY-4.0(governs the Figshare-hosted checkpoints

The checkpoints are freely downloadable and the corpus is described in the paper, but the repository ships only the code needed to embed a dataset, so the full-pipeline rungs are out of reach. The licence recorded here is the weights licence, not the repository's: the 33-layer checkpoint ships from Figshare item 24320806, whose record states CC BY 4.0, while the repository LICENSE is MIT and covers the code. Under multi_sku_rule the distributed weights govern, which resolves the tier to permissive_non_osi rather than osi. The score is unchanged - the walk reaches the fallthrough either way.

• https://raw.githubusercontent.com/snap-stanford/UCE/main/README.md recorded 2026-09-10

  Documents automatic download of the pretrained 4-layer model and a Figshare link for the 33-layer model, exposes only eval_single_anndata.py, and links processed paper datasets rather than the training corpus.

• https://www.nature.com/articles/s41586-026-10689-z recorded 2026-09-10

  Describes self-supervised training on a large corpus of cell data and the resulting Integrated Mega-scale Atlas of 36 million cells from hundreds of experiments, dozens of tissues, and eight species.

• https://raw.githubusercontent.com/snap-stanford/UCE/main/LICENSE recorded 2026-09-10

  MIT License body covering the repository and the released model files, read directly rather than from the license endpoint.