| --- |
| license: apache-2.0 |
| library_name: onnx |
| pipeline_tag: time-series-forecasting |
| base_model: amazon/chronos-2 |
| inference: false |
| tags: |
| - time-series |
| - time-series-forecasting |
| - forecasting |
| - chronos |
| - chronos-2 |
| - foundation-model |
| - onnx |
| - openstef |
| - energy |
| --- |
| |
| # chronos-2 ONNX checkpoints |
|
|
| ONNX export of [`amazon/chronos-2`](https://huggingface.co/amazon/chronos-2) for |
| [OpenSTEF](https://github.com/OpenSTEF/openstef), produced by |
| [`openstef-checkpoints`](https://github.com/OpenSTEF/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. |
|
|
| ## Variants |
|
|
| | 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. |
|
|
| ## Static and dynamic variants |
|
|
| 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: |
|
|
| - Batch: 4 series (one target plus 3 covariates) |
| - Context: 5760 steps (60 days at 15-minute resolution) |
| - Horizon: 672 steps (7 days) |
|
|
| 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. |
|
|
|
|
| ## Provenance |
|
|
| - Source model: `amazon/chronos-2` @ `unknown` |
| - Exporter: `openstef-checkpoints` @ `3b897ec44492a3ddc36c5d05b3bac5f33c26d49b` |
| - Tooling: onnx=1.22.0 onnxruntime=1.27.0 torch=2.12.1+cpu |
| - Exported: 2026-06-19T17:16:49+00:00 |
|
|
| ## License and attribution |
|
|
| These ONNX checkpoints are derived from |
| [`amazon/chronos-2`](https://huggingface.co/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`](https://github.com/OpenSTEF/openstef-checkpoints)) is licensed MPL-2.0, |
| which does not extend to the weights. |
