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OpenSTEF/chronos-2-onnx
chronos-2-onnx is a time series forecasting model from OpenSTEF. Use it for the time series forecasting task on the model card, and read the license before you ship it in a product. It is set up for onnx. The card lists the license as apache-2.0.
ONNX export of amazon/chronos-2 for OpenSTEF, produced by openstef-checkpoints.
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Updated Jun 19, 2026
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From the Hugging Face model README
ONNX export of amazon/chronos-2 for
OpenSTEF, produced by
openstef-checkpoints.
OpenSTEF resolves these via HubCheckpoint; the metadata file next to each weights file drives the
inference path. Each variant was checked against the torch reference on representative inputs before
publishing.
| File | Precision | Static shapes | Max deviation vs torch |
|---|---|---|---|
chronos-2_static.onnx | fp32 | yes | 2.241e-05 |
chronos-2.onnx | fp32 | no | 1.86e-05 |
chronos-2_int8.onnx | int8 | no | 0.7023 |
Pick by deployment target: static fp32 is the portable, CoreML-eligible default; int8 for size; dynamic when context or horizon must vary.
The dynamic variants leave the input axes free, so the context length, horizon, and number of covariate series can vary at run time. The static variants freeze every axis to a fixed size. That is what makes them eligible for CoreML and quicker to load, at the cost of only accepting the one window they were built for.
The static graph is built for:
With those sizes its ONNX inputs are fixed to:
| Input | Shape |
|---|---|
context | (4, 5760) |
group_ids | (4,) |
attention_mask | (4, 5760) |
future_covariates | (4, 672) |
future_covariates_mask | (4, 672) |
To run a different window, use a dynamic variant.
amazon/chronos-2 @ unknownopenstef-checkpoints @ 3b897ec44492a3ddc36c5d05b3bac5f33c26d49bThese ONNX checkpoints are derived from
amazon/chronos-2 and released under the same
license, apache-2.0. Attribution and all rights to the model weights remain with the
upstream authors; if you use the model in research, please cite their work.
Modifications. The weights are not retrained, fine-tuned, or otherwise changed. At the graph level the export does two things, both verified to match the reference model within the deviation shown above: it reimplements a few operators the ONNX tracer does not support (NaN-aware mean and sum, arcsinh, and the patch reshape) with equivalent ONNX ops, and it recomputes the rotary-embedding frequency buffer that the upstream loader leaves uninitialised. It then exports the result in static or dynamic shape variants at fp32 or int8 precision.
The tooling that produced these files
(openstef-checkpoints) is licensed MPL-2.0,
which does not extend to the weights.