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--- |
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tags: |
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- single-cell |
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- biology |
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- foundation-model |
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- in-context-learning |
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- transcriptomics |
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- genomics |
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--- |
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# Stack-Large |
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**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. |
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## Model Details |
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| Property | Value | |
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|----------|-------| |
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| **Parameters** | 217M | |
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| **Architecture** | Tabular Attention (alternating cell-wise and gene-wise attention) | |
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| **Model Size** | Large | |
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| **Pretraining Data** | Full human scBaseCount (~150M cells) | |
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| **Pretraining Epochs** | 10 | |
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## Usage |
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### Installation |
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```bash |
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pip install arc-stack |
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``` |
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### Download Model |
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```python |
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from huggingface_hub import snapshot_download |
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repo_id = "arcinstitute/Stack-Large" |
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local_dir = "Stack-Large" |
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snapshot_download(repo_id=repo_id, repo_type="model", local_dir=local_dir) |
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``` |
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For detailed tutorials, see: |
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- [Embedding Tutorial](https://github.com/ArcInstitute/stack/blob/main/notebooks/tutorial-embed.ipynb) |
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## Citation |
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If you use this model, please cite: |
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- Dong et al., 2026: Stack: In-context modeling of single-cell biology. Preprint. [Paper link](https://www.biorxiv.org/content/10.64898/2026.01.09.698608v1) |
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## License |
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For model licenses please see MODEL_ACCEPTABLE_USE_POLICY.md, MODEL_LICENSE.md, and LICENSE in [stack Github](https://github.com/ArcInstitute/stack). |
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## Github Link |
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[https://github.com/ArcInstitute/stack](https://github.com/ArcInstitute/stack) |
