---
title: MapPFN
emoji: 🧬
colorFrom: indigo
colorTo: red
sdk: gradio
sdk_version: 6.31.0
app_file: app.py
short_description: Predict single-cell gene perturbation effects in context
python_version: "3.12"
startup_duration_timeout: 30m
license: mit
models:
  - marvinsxtr/MapPFN
datasets:
  - marvinsxtr/MapPFN
---

# MapPFN: Learning Causal Perturbation Maps in Context

Demo of [MapPFN](https://huggingface.co/papers/2601.21092) (Sextro, Kłos & Dernbach, NeurIPS 2026): a prior-data fitted
network that predicts post-perturbation single-cell expression distributions in a biological context by reading
other perturbation experiments from that context in-context.

- Weights: [marvinsxtr/MapPFN](https://huggingface.co/marvinsxtr/MapPFN). The demo uses `model.ckpt`, the checkpoint pre-trained on the synthetic prior (EMA weights). The released fine-tuned checkpoints don't match the gene panel/order of the released data splits, so they are not used.
- Data: [marvinsxtr/MapPFN](https://huggingface.co/datasets/marvinsxtr/MapPFN) (`split_10`, 50-gene log-normalised panels).
- Code: [github.com/marvinsxtr/MapPFN](https://github.com/marvinsxtr/MapPFN) (MIT). The original JAX/Equinox model is ported 1:1 to
  PyTorch (`mappfn_torch.py`). Checkpoints are deserialised with Equinox on CPU at startup. Sampling matches the authors'
  settings: Dopri5 with a constant step of 0.01 and classifier-free guidance of 2.0.