sfourdrinier/opencgm-stateevent

Model card — opencgm_stateevent encoder (v0.1.0)

License

Component	License
Encoder (glucofm_encoder.onnx)	CC-BY-NC-4.0 (research / non-commercial)
Probe heads (glucofm_heads.json)	CC-BY-NC-SA-4.0 — share-alike inherited from CGMacros (D025)
Source code (this repo, the Python trainer, the Next.js demo)	Apache-2.0
HF Hub license: tag	other — the repo holds two artefacts under two licences

See LICENSE-WEIGHTS for the full text and LICENSE_NOTES.md for the decision record. A commercial license for the weights can be negotiated with the maintainer (@sfourdrinier on GitHub).

Summary

A 435,633-parameter dual-stream JEPA-style encoder for 24-hour continuous glucose monitoring windows. Trained on a public-data corpus; not derived from any Google implementation or checkpoint. See LICENSE_NOTES.md for weights licensing and manifests/sources/registry.yaml for the data record.

Intended use

• In scope: research on CGM-derived phenotypes (glycemic variability, insulin resistance proxies, beta-cell dysfunction proxies, glucotype clustering, hyperlipidaemia proxies, obesity proxies, diabetes risk, hypoglycaemia). Embeddings compose across days (n_days = 1..7) for cohort-level phenotypes.
• Out of scope: clinical diagnosis, treatment decisions, dosing recommendations, real-time insulin titration, alarm generation. Not a medical device.

Training data

The encoder was pretrained on Lane A only — the four public cohorts the GlucoFM paper names. These are the windows that are actually inside the released weights.

Source	Windows	Subjects	Hours	Licence
stanford	171,140	56	8,761	source repository states none
shanghai_t2dm	159,119	100	12,414	CC-BY-4.0
big_ideas	13,167	16	3,017	ODC-By-1.0
colas	9,701	68	9,544	CC-BY-4.0
total	353,127	240	33,736	30.9% of the paper's 109,066 h

Windows overlap, so the window count is much larger than hours ÷ 24; hours are the non-overlapping quantity and are what the 30.9% refers to.

No other lane entered pretraining. Hall (Lane B) and the Lane C and D cohorts are used for downstream evaluation and for the PPG teacher-student pilot, not for training the encoder. Lane E — cgmacros, uchtt1dm, glucofm_bench — is evaluation-only under non-commercial, share-alike or no-derivatives terms.

The full corpus audit is in manifests/sources/registry.yaml; the lane rules are in DECISIONS.md D002 and bundle/BLUEPRINT_AMENDMENTS.md A4.

Architecture

	value
Parameters	435,633 in this released encoder; 732,593 trainable in the full pretraining model (encoder + predictor + transition heads)
Input	24 h × 5 min grid (288 positions, 24 hourly patches × 12 steps)
Streams	state + event (causal Gaussian decomposition, learnable σ)
Embedding	64 → 128-d fused physiological token, gated with circadian time embedding
Backbone	3-layer, 4-head Transformer (paper §3.3; our D019)
Training objective	JEPA-style masked-token prediction + temporal-dynamics loss
Masking	50-60 % of patches, sampled uniformly per window, masked before normalisation and filtering
EMA target	linear ramp 0.997 → 0.9994 per epoch
Reported in paper	0.72 M — within 1.7 %

Provenance of this artifact

The weights in this repository are one checkpoint, not the five-seed ensemble the evaluation below is computed over. Read this section before quoting a number against the file you downloaded.

	value
Checkpoint	runs_5090/rawstats120/ckpt_ep040.pt
Seed	17
Epoch	40, not 120
zero_empty_patches	false — as trained (see below)
Exported file	glucofm_encoder.onnx, opset 17, 1,991,782 bytes
SHA-256	b1349deffd15ab62a5a98d7c7c4a7e143bcf1dee7ad745b1e05533ff54f34768

Why epoch 40 and not 120. Epoch 40 is the measured transfer peak at 30.9 % of the paper's corpus; epoch 120 is mildly worse (−0.005 ROC, about 1 sd of the seed spread). That is the expected consequence of giving a model more capacity than a reduced corpus can use, and it is reported rather than hidden. See D024 and findings/results_section.md.

Why zero_empty_patches is false here. D020 — an empty patch emits nothing rather than a learned bias — is tagged PAPER_EXACT and is now the repo default (True). This checkpoint predates that change, so it was trained with the learned bias and is exported faithfully as such. D020 was measured to be neutral (0.6780 vs 0.6818, well inside the seed sd), so the released weights are not disadvantaged by it — but if you rebuild the architecture from the current repo defaults, you will not get an architecture that matches this file. Construct it with zero_empty_patches=False, or read the flags from glucofm_encoder.onnx.meta.json, which records the exact architecture this artifact was exported under.

Which probe heads are distributed

All 18 probe heads are published; 14 clear the signal floor. The bundle (glucofm_heads.json) is licensed CC-BY-NC-SA-4.0, one step stricter than the encoder.

Eight heads are fitted on CGMacros, whose registry entry reads CC-BY-NC-SA-4.0 with license_confidence: unverified (open question Q4). Share-alike obliges an adapted work to be offered under the same licence, so the bundle carries it. Keeping the heads as a separate artefact from the encoder stops that term reaching the encoder, which never saw CGMacros and stays CC-BY-NC-4.0.

Three heads are fitted on Stanford. They are published: Stanford is Lane A and already inside the encoder, so withholding classifiers fitted on its labels would protect nothing.

Anything fitted on UCHTT1DM would stay out permanently — its no-derivatives term has no labelling remedy. No such head exists today; the filter enforces it regardless.

See D025. Enforced by tests/unit/test_source_rights.py::test_published_heads_bundle_declares_the_licence_its_sources_impose.

Evaluation (the headline numbers)

The table below is the five-seed, 120-epoch protocol — the scientific result of the project. It characterises the method, not the single file above. A single ep40 checkpoint will land near, but not exactly on, these numbers.

5 seeds × 120 epochs, subject-disjoint 5×10-fold probe, macro across 14 dataset-task probes (18 task-source combinations — two cohorts are measured on two sensors each):

Method	ROC-AUC (mean ± sd)	PR-AUC (mean ± sd)
clinical_metrics baseline	0.643	—
raw_masked baseline	0.607	—
opencgm_mean (this model)	0.670 ± 0.003	0.588 ± 0.003
CGM-JEPA (the paper's central comparator)	0.643 ± 0.004	0.566 ± 0.003

The paper reports 0.74 ROC across 14 tasks on its full corpus; we measure 0.670 on 30.9 % of the corpus. Direction and ordering of effects survive; absolute magnitude is smaller. See findings/results_section.md §8–§9 for the gap analysis.

Ablation evidence

The 30-run Tier-1 ablation (3 seeds × 10 conditions × 40 epochs) confirms the load-bearing role of the dual-stream decomposition: removing the event stream costs −0.050 ROC (largest single ablator), removing the state stream costs −0.014, and, the circadian embedding costs −0.015. Single-stream baselines (abl_raw, abl_event, abl_state) are the only ablations beyond −0.02.

The abl_dense arm (forced interpolation, which the production rule forbids) is statistically indistinguishable from the observed mask at 40 epochs (+0.0005). The never-interpolate rule is not a hair-shirt; it is the principled choice that does not also need ablation to defend it.

Caveats and limits

• 30.9 % of paper pretraining hours. Wear-CGM (two unreleased Google/Fitbit cohorts corpus) is non-public. The released encoder cannot match the paper's absolute scores.
• Stanford's source repository states no licence. It is in the released weights, and recorded as such in manifests/sources/registry.yaml.
• Single-seed multi-day, cross-dataset, and few-shot extensions. These are documented in findings/results_section.md and will gain error bars when the multi-seed sweep finishes.
• No real-time inference. The encoder expects 24-hour windows; real-time per-reading inference is out of scope.

Citation

@software{opencgm_stateevent_2026,
  title  = {OpenCGM-StateEvent: independent public-data reconstruction of GlucoFM},
  author = {Fourdrinier, Stephane},
  year   = {2026},
  url    = {https://github.com/sfourdrinier/opencgm-stateevent},
  note   = {Apache-2.0; see CITATION.cff}
}

Plus cite the original method:

@article{glucofm_2026,
  title  = {GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring},
  url    = {https://arxiv.org/abs/2605.30865v2}
}