Hypnos

Hypnos is an autoregressive RQ-Transformer trained via next-token prediction on tokenized streams of physiological sensor data. It produces general-purpose 1 Hz embeddings of sleep physiology for downstream tasks (sleep staging, arousal/event detection, etc.).

This repo holds the released model as a single weight-only safetensors bundle: the RQ-Transformer and all 5 tokenizers, plus the config (model + tokenizer construction kwargs, modality layout) in the file metadata — so loading is fully self-contained.

Usage

Use the hypnos inference library:

from hypnos.embedding import embed_edf

emb = embed_edf("recording.edf")   # defaults to this repo
# emb: dict {modality_name: np.ndarray [n_seconds, embed_dim] float16}
#   e.g. emb["eeg_c3"], emb["ecg"], ... — one vector per second, per present modality

Embeddings are returned per modality at the model's native 1 Hz cadence (one vector per second). Only modalities present in the recording appear in the dict. For US recordings pass notch_freq=60.0 (the default is 50 Hz).

Pooling

The 1 Hz per-modality output is the raw signal; pool it however your task needs — e.g. a single embedding per 30-second sleep epoch, averaged over modalities and time:

import numpy as np

emb = embed_edf("recording.edf")
fused = np.mean(list(emb.values()), axis=0)              # over modalities -> [T, D]
n = fused.shape[0] // 30
epochs = fused[: n * 30].reshape(n, 30, -1).mean(axis=1)  # over 30-s epochs -> [T//30, D]

Modalities

8 modalities sharing 5 tokenizers (K = RVQ levels, all codebooks size 2048, 1 token/sec):

EEG/EOG channels are contralaterally referenced (e.g. C3-M2); Chin EMG is a bipolar derivation; ECG/respiratory are used directly. Embedding dimension is 768.

Format

hypnos.safetensors — weights under namespaced keys (model/…, tok/<name>/…) with the config as a JSON string in the file metadata. Loaded with safetensors (no arbitrary-code unpickling).

License

Released under the MIT License.

Citation

@inproceedings{carterHypnos2026,
  title         = {Hypnos: Learning Generalisable Representations of Sleep Physiology via Next-Token Prediction},
  author        = {Carter, Jonathan F. and Tarassenko, Lionel},
  booktitle     = {Advances in Neural Information Processing Systems},
  year          = {2026},
  eprint        = {2606.09605},
  archivePrefix = {arXiv},
}