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arcinstitute/
Stack-Large-Aligned

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Stack-Large-Aligned

Stack is a large-scale encoder-decoder foundation model for single-cell biology. It introduces a novel tabular attention architecture that enables both intra- and inter-cellular information flow, setting cell-by-gene matrix chunks as the basic input data unit. Through in-context learning, Stack offers substantial performance improvements in generalizing biological effects and enables generation of unseen cell profiles in novel contexts.

Model Details

Property	Value
Parameters	217M
Architecture	Tabular Attention (alternating cell-wise and gene-wise attention)
Model Size	Large
Pretraining Data	Full human scBaseCount (~150M cells)
Pretraining Epochs	10
Alignment Data	CellxGene 45M + Parse 10M PBMC
Alignment Epochs	8

Usage

Installation

pip install arc-stack

Download Model

python

from huggingface_hub import snapshot_download

repo_id = "arcinstitute/Stack-Large-Aligned"
local_dir = "Stack-Large-Aligned"
snapshot_download(repo_id=repo_id, repo_type="model", local_dir=local_dir)

For detailed tutorials, see:

Prediction Tutorial

Citation

If you use this model, please cite:

Dong et al., 2026: Stack: In-context modeling of single-cell biology. Preprint. Paper link

License

This model is released under a non-commercial license.

For model licenses please see MODEL_ACCEPTABLE_USE_POLICY.md, MODEL_LICENSE.md, and LICENSE in stack Github.

Github Link

https://github.com/ArcInstitute/stack