Update model link to Hugging Face (#353)

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*Description of changes:*


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.github/workflows/eval-model.yml

@@ -36,13 +36,13 @@ jobs:
         run: pip install ".[dev]" -f https://download.pytorch.org/whl/cpu/torch_stable.html
 
       - name: Run Eval Script for Chronos-2
-        run: python scripts/evaluation/evaluate.py chronos-2 ci/evaluate/backtest_config.yaml $CHRONOS_2_RESULTS_CSV --model-id=s3://autogluon/chronos-2 --device=cpu --torch-dtype=float32
+        run: python scripts/evaluation/evaluate.py chronos-2 ci/evaluate/backtest_config.yaml $CHRONOS_2_RESULTS_CSV --model-id=amazon/chronos-2 --device=cpu --torch-dtype=float32
 
       - name: Print Chronos-2 CSV
         run: cat $CHRONOS_2_RESULTS_CSV
 
       - name: Run Eval Script for Chronos-Bolt
         run: python scripts/evaluation/evaluate.py chronos-bolt ci/evaluate/backtest_config.yaml $CHRONOS_BOLT_RESULTS_CSV --model-id=amazon/chronos-bolt-small --device=cpu --torch-dtype=float32
-      
+
       - name: Print Chronos-Bolt CSV
         run: cat $CHRONOS_BOLT_RESULTS_CSV

README.md

@@ -19,7 +19,7 @@
 
 
 ## 🚀 News
-- **20 Oct 2025**: 🚀 [Chronos-2](https://arxiv.org/abs/2510.15821) released. It offers _zero-shot_ support for univariate, multivariate, and covariate-informed forecasting tasks. Chronos-2 achieves the best performance on fev-bench, GIFT-Eval and Chronos Benchmark II amongst pretrained models. Check out [this notebook](notebooks/chronos-2-quickstart.ipynb) to get started with Chronos-2.
+- **20 Oct 2025**: 🚀 [Chronos-2](https://huggingface.co/amazon/chronos-2) released. It offers _zero-shot_ support for univariate, multivariate, and covariate-informed forecasting tasks. Chronos-2 achieves the best performance on fev-bench, GIFT-Eval and Chronos Benchmark II amongst pretrained models. Check out [this notebook](notebooks/chronos-2-quickstart.ipynb) to get started with Chronos-2.
 - **14 Feb 2025**: 🚀 Chronos-Bolt is now available on Amazon SageMaker JumpStart! Check out the [tutorial notebook](notebooks/deploy-chronos-bolt-to-amazon-sagemaker.ipynb) to learn how to deploy Chronos endpoints for production use in 3 lines of code.
 - **12 Dec 2024**: 📊 We released [`fev`](https://github.com/autogluon/fev), a lightweight package for benchmarking time series forecasting models based on the [Hugging Face `datasets`](https://huggingface.co/docs/datasets/en/index) library.
 - **26 Nov 2024**: ⚡️ Chronos-Bolt models released [on HuggingFace](https://huggingface.co/collections/amazon/chronos-models-65f1791d630a8d57cb718444). Chronos-Bolt models are more accurate (5% lower error), up to 250x faster and 20x more memory efficient than the original Chronos models of the same size!
@@ -39,7 +39,7 @@ This package provides an interface to the Chronos family of **pretrained time se
 
 | Model ID                                                               | Parameters |
 | ---------------------------------------------------------------------- | ---------- |
-| [`s3://autogluon/chronos-2`](https://arxiv.org/abs/2510.15821)   | 120M         |
+| [`amazon/chronos-2`](https://huggingface.co/amazon/chronos-2)   | 120M         |
 | [`amazon/chronos-bolt-tiny`](https://huggingface.co/amazon/chronos-bolt-tiny)   | 9M         |
 | [`amazon/chronos-bolt-mini`](https://huggingface.co/amazon/chronos-bolt-mini)   | 21M        |
 | [`amazon/chronos-bolt-small`](https://huggingface.co/amazon/chronos-bolt-small) | 48M        |
@@ -48,7 +48,7 @@ This package provides an interface to the Chronos family of **pretrained time se
 | [`amazon/chronos-t5-mini`](https://huggingface.co/amazon/chronos-t5-mini)   | 20M        |
 | [`amazon/chronos-t5-small`](https://huggingface.co/amazon/chronos-t5-small) | 46M        |
 | [`amazon/chronos-t5-base`](https://huggingface.co/amazon/chronos-t5-base)   | 200M       |
-| [`amazon/chronos-t5-large`](https://huggingface.co/amazon/chronos-t5-large) | 710M       | 
+| [`amazon/chronos-t5-large`](https://huggingface.co/amazon/chronos-t5-large) | 710M       |
 
 </div>
 
@@ -68,7 +68,7 @@ A minimal example showing how to perform forecasting using Chronos-2:
 import pandas as pd  # requires: pip install 'pandas[pyarrow]'
 from chronos import Chronos2Pipeline
 
-pipeline = Chronos2Pipeline.from_pretrained("s3://autogluon/chronos-2", device_map="cuda")
+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")

notebooks/chronos-2-quickstart.ipynb

@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "fcc7e496",
    "metadata": {},
    "outputs": [],
@@ -53,7 +53,7 @@
     "\n",
     "# Load the Chronos-2 pipeline\n",
     "# GPU recommended for faster inference, but CPU is also supported\n",
-    "pipeline: Chronos2Pipeline = BaseChronosPipeline.from_pretrained(\"s3://autogluon/chronos-2/\", device_map=\"cuda\")"
+    "pipeline: Chronos2Pipeline = BaseChronosPipeline.from_pretrained(\"amazon/chronos-2\", device_map=\"cuda\")"
    ]
   },
   {

test/test_chronos2.py

@@ -13,10 +13,9 @@ import pytest
 import torch
 
 from chronos import BaseChronosPipeline, Chronos2Pipeline
-from chronos.chronos2.dataset import convert_df_input_to_list_of_dicts_input
 from chronos.chronos2.config import Chronos2CoreConfig
+from chronos.chronos2.dataset import convert_df_input_to_list_of_dicts_input
 from chronos.chronos2.layers import MHA
-
 from test.util import validate_tensor
 
 DUMMY_MODEL_PATH = Path(__file__).parent / "dummy-chronos2-model"
@@ -35,6 +34,10 @@ def test_base_chronos2_pipeline_loads_from_s3():
     BaseChronosPipeline.from_pretrained("s3://autogluon/chronos-2", device_map="cpu")
 
 
+def test_base_chronos2_pipeline_loads_from_hf():
+    BaseChronosPipeline.from_pretrained("amazon/chronos-2", device_map="cpu")
+
+
 @pytest.mark.parametrize(
     "inputs, prediction_length, expected_output_shapes",
     [