README.md

---
license: mit
language:
- en
library_name: jax
tags:
- perturbation-prediction
- prior-data-fitted-networks
- in-context-learning
- single-cell
- causal-inference
- diffusion-transformer
datasets:
- marvinsxtr/MapPFN
pipeline_tag: other
---

# MapPFN Weights

Pre-trained and fine-tuned checkpoints for [MapPFN: Learning Causal Perturbation Maps in Context](https://arxiv.org/abs/2601.21092) (Sextro et al., 2026).

## Checkpoints

- `model.ckpt` — Pre-trained on synthetic biological prior (50 dimensions, 400k steps)
- `model_finetuned_frangieh.ckpt` — Fine-tuned on [Frangieh et al. (2021)](https://doi.org/10.1038/s41588-021-00779-1)
- `model_finetuned_papalexi.ckpt` — Fine-tuned on [Papalexi et al. (2021)](https://doi.org/10.1038/s41588-021-00778-2)

All checkpoints share the same MMDiT architecture (~25M parameters) and differ only in training data. See the [GitHub repository](https://github.com/marvinsxtr/MapPFN) for inference and fine-tuning code.

## Citation

```bibtex
@article{sextro2026mappfn,
  title   = {{MapPFN}: Learning Causal Perturbation Maps in Context},
  author  = {Sextro, Marvin and K\l{}os, Weronika and Dernbach, Gabriel},
  journal = {arXiv preprint arXiv:2601.21092},
  year    = {2026}
}
```

**Links:** [Paper](https://arxiv.org/abs/2601.21092) | [Code](https://github.com/marvinsxtr/MapPFN) | [Datasets](https://huggingface.co/datasets/marvinsxtr/MapPFN) | [Project Page](https://marvinsxtr.github.io/MapPFN)