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MyHeartCounts/openmhc-lsm2-weekly-sparse
openmhc-lsm2-weekly-sparse is a feature extraction model from MyHeartCounts. Use it when you need embeddings to search or compare text. It is set up for openmhc. The card lists the license as openrail.
Reference checkpoint for OpenMHC. Window: weekly (10080 minute) sparse-decoder, 19 sensor channels.
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Updated May 29, 2026
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From the Hugging Face model README
Reference checkpoint for OpenMHC. Window: weekly (10080 minute) sparse-decoder, 19 sensor channels.
A sparse-decoder extension of LSM2 for 7-day inputs. Instead of one transformer over the full week, this variant applies the frozen daily encoder (LSM2-daily) independently per-day and reconstructs the week through alternating day-local / cross-day decoder blocks (optionally calendar-aware via RoPE day embeddings). Lower compute than the dense weekly variant. Wrapped here for the OpenMHC imputation evaluation track.
from openmhc.imputers import LSM2WeeklySparseImputer
import openmhc
imp = LSM2WeeklySparseImputer.from_release("hf://MyHeartCounts/openmhc-lsm2-weekly-sparse")
results = openmhc.evaluate_imputation(imp, version="xs")
print(results.summary())
Requires the matching optional extras: pip install 'openmhc[lsm2,hf]'.
Pin a specific revision with the @ suffix:
LSM2WeeklySparseImputer.from_release("hf://MyHeartCounts/[email protected]")
MHC_Dataset/mhc-mae-ssl/mae-weekly-sparse-d4:v0openmhc_manifest.json.Released under the OpenRAIL license. See the OpenMHC repository for use restrictions tied to the underlying data agreement.
@misc{openmhc,
title = {OpenMHC: Accelerating the Science of Wearable Foundation Models},
author = {OpenMHC team},
url = {https://github.com/AshleyLab/myheartcounts-dataset}
}