metadata
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 (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)model_finetuned_papalexi.ckpt— Fine-tuned on Papalexi et al. (2021)
All checkpoints share the same MMDiT architecture (~25M parameters) and differ only in training data. See the GitHub repository for inference and fine-tuning code.
Citation
@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 | Code | Datasets | Project Page