Add Chronos-2 converted to safetensors

LICENSE

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

NOTICE

@@ -0,0 +1,6 @@
+Chronos-2 by Amazon, from https://huggingface.co/amazon/chronos-2 (revision 29ec3766d36d6f73f0696f85560a422f50e8498c), licensed under the Apache License, Version 2.0 in LICENSE.
+Copyright Amazon.com, Inc. or its affiliates.
+
+General Forecast changed the files: config.json was rewritten as generalforecast's architecture_name and config, and model.safetensors was re-saved under that layout. Every weight value is unchanged.
+
+This is not an official Amazon release.

README.md

@@ -0,0 +1,28 @@
+---
+license: apache-2.0
+base_model: amazon/chronos-2
+library_name: generalforecast
+pipeline_tag: time-series-forecasting
+tags:
+- time-series
+- forecasting
+- chronos
+---
+
+# Chronos-2
+
+Amazon's Chronos-2 ([`amazon/chronos-2`](https://huggingface.co/amazon/chronos-2) at revision `29ec3766d36d`), converted to safetensors for [generalforecast](https://github.com/generalforecast/gfi). The weights are unchanged.
+
+This is not an official Amazon release and is not endorsed by Amazon.
+
+## Files
+
+- `model.safetensors`: weights, under Amazon's PyTorch parameter names.
+- `config.json`: `architecture_name` and `config`, the upstream model config.
+- `LICENSE`: the Apache License, Version 2.0.
+- `NOTICE`: attribution, and what was changed.
+- `UPSTREAM-README.md`: the upstream model card, unchanged.
+
+## License
+
+The Apache License, Version 2.0, in `LICENSE`, as declared by the upstream model card (kept as `UPSTREAM-README.md`).

UPSTREAM-README.md

@@ -0,0 +1,180 @@
+---
+license: apache-2.0
+model_id: chronos-2
+tags:
+  - time series
+  - forecasting
+  - foundation models
+  - pretrained models
+  - safetensors
+paper:
+  - https://arxiv.org/abs/2510.15821
+datasets:
+  - autogluon/chronos_datasets
+  - Salesforce/GiftEvalPretrain
+leaderboards:
+  - Salesforce/GIFT-Eval
+  - autogluon/fev-leaderboard
+pipeline_tag: time-series-forecasting
+library_name: chronos-forecasting
+
+---
+
+# Chronos-2
+
+**Update Jun 5, 2026:** ☁️ Deploy Chronos-2 on AWS with AutoGluon-Cloud. Real-time, serverless, or batch inference in 3 lines of code — pandas DataFrames in, forecasts out. Check out the [new deployment guide](https://auto.gluon.ai/cloud/stable/tutorials/foundation-model-timeseries.html).
+
+**Chronos-2** is a 120M-parameter, encoder-only time series foundation model for zero-shot forecasting.
+It supports **univariate**, **multivariate**, and **covariate-informed** tasks within a single architecture.
+Inspired by the T5 encoder, Chronos-2 produces multi-step-ahead quantile forecasts and uses a group attention mechanism for efficient in-context learning across related series and covariates.
+Trained on a combination of real-world and large-scale synthetic datasets, it achieves **state-of-the-art zero-shot accuracy** among public models on [**fev-bench**](https://huggingface.co/spaces/autogluon/fev-leaderboard), [**GIFT-Eval**](https://huggingface.co/spaces/Salesforce/GIFT-Eval), and [**Chronos Benchmark II**](https://arxiv.org/abs/2403.07815).
+Chronos-2 is also **highly efficient**, delivering over 300 time series forecasts per second on a single A10G GPU and supporting both **GPU and CPU inference**.
+
+## Links
+- ☁️ [Deploy on SageMaker with AutoGluon-Cloud](https://auto.gluon.ai/cloud/stable/tutorials/foundation-model-timeseries.html) (recommended)
+- 🚀 [Deploy on SageMaker with JumpStart](https://github.com/amazon-science/chronos-forecasting/blob/main/notebooks/deploy-chronos-to-amazon-sagemaker.ipynb)
+- 📄 [Technical report](https://arxiv.org/abs/2510.15821v1)
+- 💻 [GitHub](https://github.com/amazon-science/chronos-forecasting)
+- 📘 [Example notebook](https://github.com/amazon-science/chronos-forecasting/blob/main/notebooks/chronos-2-quickstart.ipynb)
+- 📰 [Amazon Science Blog](https://www.amazon.science/blog/introducing-chronos-2-from-univariate-to-universal-forecasting)
+
+
+## Overview
+
+| Capability | Chronos-2 | Chronos-Bolt | Chronos |
+|------------|-----------|--------------|----------|
+| Univariate Forecasting | ✅ | ✅ | ✅ |
+| Cross-learning across items | ✅ | ❌ | ❌ |
+| Multivariate Forecasting | ✅ | ❌ | ❌ |
+| Past-only (real/categorical) covariates | ✅ | ❌ | ❌ |
+| Known future (real/categorical) covariates | ✅ | 🧩 | 🧩 |
+| Max. Context Length | 8192 | 2048 | 512 |
+| Max. Prediction Length | 1024 | 64 | 64 |
+
+🧩 Chronos & Chronos-Bolt do not natively support future covariates, but they can be combined with external covariate regressors (see [AutoGluon tutorial](https://auto.gluon.ai/1.4.0/tutorials/timeseries/forecasting-chronos.html#incorporating-the-covariates)). This only models per-timestep effects, not effects across time. In contrast, Chronos-2 supports all covariate types natively.
+
+
+## Running the model locally
+
+For experimentation and local inference, you can use the [inference package](https://github.com/amazon-science/chronos-forecasting).
+
+Install the package
+```
+pip install "chronos-forecasting>=2.0"
+```
+
+Make zero-shot predictions using the `pandas` API
+
+```python
+import pandas as pd  # requires: pip install 'pandas[pyarrow]'
+from chronos import Chronos2Pipeline
+
+pipeline = Chronos2Pipeline.from_pretrained("amazon/chronos-2", device_map="cuda")
+
+# Load historical target values and past values of covariates
+context_df = pd.read_parquet("https://autogluon.s3.amazonaws.com/datasets/timeseries/electricity_price/train.parquet")
+
+# (Optional) Load future values of covariates
+future_df = pd.read_parquet("https://autogluon.s3.amazonaws.com/datasets/timeseries/electricity_price/test.parquet").drop(columns="target")
+
+# Generate predictions with covariates
+pred_df = pipeline.predict_df(
+    context_df,
+    future_df=future_df,
+    prediction_length=24,  # Number of steps to forecast
+    quantile_levels=[0.1, 0.5, 0.9],  # Quantiles for probabilistic forecast
+    id_column="id",  # Column identifying different time series
+    timestamp_column="timestamp",  # Column with datetime information
+    target="target",  # Column(s) with time series values to predict
+)
+```
+
+## Production use on Amazon SageMaker
+
+For production use, we recommend deploying Chronos-2 to Amazon SageMaker. There are two options:
+
+- **AutoGluon-Cloud** (recommended) — minimal setup with a high-level Python API: pass a pandas DataFrame in, get forecasts back. Supports real-time, serverless, and batch inference out of the box.
+- **SageMaker JumpStart** — fine-grained control over the deployment configuration. JSON request/response payloads only; serverless inference and batch prediction require additional setup.
+
+### ☁️ AutoGluon-Cloud
+
+Install AutoGluon-Cloud:
+
+```
+pip install autogluon.cloud>=0.5.0
+```
+
+Make predictions from a pandas DataFrame
+
+```python
+from autogluon.cloud import TimeSeriesFoundationModel
+
+model = TimeSeriesFoundationModel(model_name="chronos-2")
+
+# Batch prediction
+forecast_df = model.predict(df, prediction_length=24)
+
+# Deploy & invoke a real-time endpoint
+endpoint = model.deploy(instance_type="ml.g5.xlarge")
+forecast_df = endpoint.predict(df, prediction_length=24)
+```
+
+For more details (e.g. serverless endpoints, covariate-aware forecasting), see the [full deployment guide](https://auto.gluon.ai/cloud/stable/tutorials/foundation-model-timeseries.html).
+
+### 🚀 SageMaker JumpStart
+
+First, update the SageMaker SDK to make sure that all the latest models are available.
+
+```
+pip install -U 'sagemaker<3'
+```
+
+Deploy an inference endpoint to SageMaker.
+
+```python
+from sagemaker.jumpstart.model import JumpStartModel
+
+model = JumpStartModel(
+    model_id="pytorch-forecasting-chronos-2",
+    instance_type="ml.g5.2xlarge",
+)
+predictor = model.deploy()
+```
+
+Now you can send time series data to the endpoint in JSON format.
+
+```python
+payload = {
+    "inputs": [
+        {"target": [1.0, 2.5, ..., 12.3]}
+    ],
+    "parameters": {
+        "prediction_length": 24,
+    }
+}
+forecast = predictor.predict(payload)["predictions"]
+```
+
+For more details about the endpoint API, check out the [example notebook](https://github.com/amazon-science/chronos-forecasting/blob/2.2.2/notebooks/deploy-chronos-to-amazon-sagemaker.ipynb).
+
+
+## Training data
+More details about the training data are available in the [technical report](https://arxiv.org/abs/2510.15821).
+
+- Subset of [Chronos Datasets](https://huggingface.co/datasets/autogluon/chronos_datasets) (excluding test portion of datasets that overlap with GIFT-Eval)
+- Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain)
+- Synthetic univariate and multivariate data
+
+
+## Citation
+
+If you find Chronos-2 useful for your research, please consider citing the associated paper:
+
+```
+@article{ansari2025chronos2,
+  title        = {Chronos-2: From Univariate to Universal Forecasting},
+  author       = {Abdul Fatir Ansari and Oleksandr Shchur and Jaris Küken and Andreas Auer and Boran Han and Pedro Mercado and Syama Sundar Rangapuram and Huibin Shen and Lorenzo Stella and Xiyuan Zhang and Mononito Goswami and Shubham Kapoor and Danielle C. Maddix and Pablo Guerron and Tony Hu and Junming Yin and Nick Erickson and Prateek Mutalik Desai and Hao Wang and Huzefa Rangwala and George Karypis and Yuyang Wang and Michael Bohlke-Schneider},
+  year         = {2025},
+  url          = {https://arxiv.org/abs/2510.15821}
+}
+```

config.json

@@ -0,0 +1,61 @@
+{
+  "architecture_name": "chronos2",
+  "config": {
+    "architectures": [
+      "Chronos2Model"
+    ],
+    "chronos_config": {
+      "context_length": 8192,
+      "input_patch_size": 16,
+      "input_patch_stride": 16,
+      "max_output_patches": 64,
+      "output_patch_size": 16,
+      "quantiles": [
+        0.01,
+        0.05,
+        0.1,
+        0.15,
+        0.2,
+        0.25,
+        0.3,
+        0.35,
+        0.4,
+        0.45,
+        0.5,
+        0.55,
+        0.6,
+        0.65,
+        0.7,
+        0.75,
+        0.8,
+        0.85,
+        0.9,
+        0.95,
+        0.99
+      ],
+      "time_encoding_scale": 8192,
+      "use_arcsinh": true,
+      "use_reg_token": true
+    },
+    "chronos_pipeline_class": "Chronos2Pipeline",
+    "d_ff": 3072,
+    "d_kv": 64,
+    "d_model": 768,
+    "dense_act_fn": "relu",
+    "dropout_rate": 0.1,
+    "feed_forward_proj": "relu",
+    "initializer_factor": 0.05,
+    "is_gated_act": false,
+    "layer_norm_epsilon": 1e-06,
+    "model_type": "t5",
+    "num_heads": 12,
+    "num_layers": 12,
+    "pad_token_id": 0,
+    "reg_token_id": 1,
+    "rope_theta": 10000.0,
+    "torch_dtype": "float32",
+    "transformers_version": "4.49.0",
+    "use_cache": true,
+    "vocab_size": 2
+  }
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d353b01a3a939d324ea3abfb8f11cfba6e817b57db9b30a23b0def70528a087
+size 477930440