SleepFM — pretrained checkpoints

Mirror of the official SleepFM checkpoints, 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

Files

File	Description	Used by
model_base/best.pt	Pretrained channel-agnostic PSG encoder (backbone)	SleepFM.load_pretrained_backbone, SleepFMStager.load_pretrained_backbone
model_sleep_staging/best.pth	Downstream sleep-staging head	SleepFMStager.load_pretrained_staging_head

These are byte-for-byte copies of the upstream artifacts; only the hosting location changed. The layout (model_base/, model_sleep_staging/) mirrors the upstream repo.

Usage

import torch
from braindecode.models import SleepFMStager

base = torch.hub.load_state_dict_from_url(
    "https://huggingface.co/braindecode/SleepFM/resolve/main/model_base/best.pt",
    map_location="cpu",
)
staging = torch.hub.load_state_dict_from_url(
    "https://huggingface.co/braindecode/SleepFM/resolve/main/model_sleep_staging/best.pth",
    map_location="cpu",
)

model = SleepFMStager(n_chans=4, n_outputs=5, n_times=3840, sfreq=128)
model.load_pretrained_backbone(base)
model.load_pretrained_staging_head(staging)
model.eval()

License & attribution

• License: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
• Copyright (c) 2025 Rahul Thapa.
• Upstream source: https://github.com/zou-group/sleepfm-clinical

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.