STACK
Collection
Stack is a single-cell foundation model that enables in-context learning at inference time.
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5 items
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Updated
Stack-Large
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 |
Usage
Installation
pip install arc-stack
Download Model
from huggingface_hub import snapshot_download
repo_id = "arcinstitute/Stack-Large"
local_dir = "Stack-Large"
snapshot_download(repo_id=repo_id, repo_type="model", local_dir=local_dir)
For detailed tutorials, see:
Citation
If you use this model, please cite:
- Dong et al., 2026: Stack: In-context modeling of single-cell biology. Preprint. Paper link
License
For model licenses please see MODEL_ACCEPTABLE_USE_POLICY.md, MODEL_LICENSE.md, and LICENSE in stack Github.
Github Link
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