Create README.md

README.md

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+---
+tags:
+- single-cell
+- biology
+- foundation-model
+- in-context-learning
+- transcriptomics
+- genomics
+---
+
+# 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
+```bash
+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](https://github.com/ArcInstitute/stack/blob/main/notebooks/tutorial-predict.ipynb)
+
+
+## Citation
+
+If you use this model, please cite:
+
+- Dong et al., 2025: STACK: In-context modeling of single-cell biology. Preprint.
+
+## License
+
+For model licenses please see MODEL_ACCEPTABLE_USE_POLICY.md, MODEL_LICENSE.md, and LICENSE in [stack Github](https://github.com/ArcInstitute/stack).
+
+## Github Link
+
+[https://github.com/ArcInstitute/stack](https://github.com/ArcInstitute/stack)