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Running on Zero
| 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. | |