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MyHeartCounts/
openmhc-chronos2-fc

Name: openmhc-chronos2-fc
Availability: InStock

openmhc

safetensors

time-series-forecasting

wearables

2510.15821

cc-by-4.0

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OpenMHC Forecasting — Chronos-2

Track 3 (forecasting) reference checkpoint for the MyHeartCounts / OpenMHC wearable-health benchmark.

This checkpoint is a Chronos-2 model. Chronos-2 is Amazon's universal time-series foundation model, supporting multivariate and covariate-informed probabilistic forecasting.

Fine-tuned from the pretrained amazon/chronos-2 base on the MHC training split (LoRA, rank 8, alpha 16; the adapter has been merged into the base so this is a standalone full model — no PEFT runtime dependency).

Checkpoint format: HuggingFace model directory (checkpoint/: config.json + model.safetensors)
Forecasting task: 24-hour-ahead, 19 sensor channels, hourly resolution.

Model & implementation

Usage

python

import openmhc
from openmhc.forecasters import Chronos2Forecaster

# pip install "openmhc[chronos]"
fc = Chronos2Forecaster.from_release("hf://MyHeartCounts/openmhc-chronos2-fc@v1.0")
results = openmhc.evaluate_forecasting(fc, version="full")

The same bundle also loads in the evaluation harness via model.release_dir=hf://MyHeartCounts/openmhc-chronos2-fc@v1.0. See openmhc_manifest.json for provenance (training run, base model, fine-tuning details) and architecture metadata.

Citation

If you use this checkpoint, please cite the OpenMHC benchmark and the original Chronos-2 work (linked above).