SleepFM โ€” pretrained encoder

Mirror of the official SleepFM encoder checkpoint, re-hosted for stable loading from Braindecode.

SleepFM is a multimodal polysomnography (PSG) foundation model introduced in:

R. Thapa et al., "A multimodal sleep foundation model for disease prediction," Nature Medicine (2026). https://doi.org/10.1038/s41591-025-04133-4

The downstream sleep stager lives in a separate repository, braindecode/SleepFMStager, because a Braindecode config.json describes exactly one architecture.

Files

File Description
model.safetensors The encoder, with the parameter names of braindecode.models.SleepFM
config.json Architecture of the checkpoint, read by from_pretrained()
model_base/best.pt The upstream artifact, byte-for-byte, kept for provenance
model_sleep_staging/best.pth The upstream staging artifact, byte-for-byte (see SleepFMStager)

model.safetensors holds the same tensors as model_base/best.pt; only the keys were rewritten (the module. prefix of the distributed training run stripped, and positional_encoding.pe renamed) so that the library needs no remapping code at load time. Loading either way gives bit-identical outputs.

Note that the released encoder is contrastive and carries no classification head: final_layer is randomly initialised and must be fine-tuned.

Usage

from braindecode.models import SleepFM

# Defaults to this repository.
model = SleepFM.from_pretrained(n_chans=4, n_outputs=5, n_times=3840, sfreq=128)
model.eval()

Input must be sampled at 128 Hz; the reference patch_size=640 is a 5-second patch at that rate. A channel mask of shape (batch, n_chans) marks missing channels with True.

License & attribution

These weights are not covered by Braindecode's BSD-3 license and inherit the upstream noncommercial terms. Re-hosted for reproducibility and stable availability only; attribution and the CC BY-NC 4.0 restriction are preserved.

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