openstef-foundation-models
Foundation model support for OpenSTEF — bringing pre-trained, ONNX-based forecasting models to the OpenSTEF ecosystem.
Installation
Pick exactly one ONNX runtime — [cpu] and [gpu] are mutually exclusive.
CPU (default — the meta-package openstef installs this for you):
pip install "openstef-foundation-models[cpu]"
GPU (CUDA):
pip install "openstef-foundation-models[gpu]"
Note: Do not install both
[cpu]and[gpu]in the same environment —onnxruntimeandonnxruntime-gpucollide. They're declared as conflicting extras so uv enforces the choice;pipdoes not, so pick one yourself.
Selecting a checkpoint
OpenSTEF publishes its checkpoints to the HuggingFace Hub. Pick a model size and variant from the catalog instead of hand-writing repo ids and filenames:
from openstef_foundation_models.models import Chronos2, CheckpointVariant
from openstef_foundation_models.presets import ForecastingWorkflowConfig
# Default: the base Chronos-2, dynamic shapes — runs on any provider.
config = ForecastingWorkflowConfig()
# The smaller model, static shapes — enables the CoreML provider on macOS.
config = ForecastingWorkflowConfig(
checkpoint=Chronos2.SMALL.checkpoint(CheckpointVariant.STATIC),
)
# Let the host decide: static on macOS, dynamic elsewhere.
config = ForecastingWorkflowConfig(
checkpoint=Chronos2.BASE.checkpoint(CheckpointVariant.recommended()),
)
Available sizes are Chronos2.BASE (chronos-2) and Chronos2.SMALL
(chronos-2-small); variants are DYNAMIC (portable) and STATIC (frozen shapes,
the macOS/CoreML path in the default provider fallback chain).
1 maintainer
Metadata
1 maintainer
Release files for openstef-foundation-models 4.4.6
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