Update README

README.md

@@ -12,7 +12,7 @@ tags:
 
 # Chronos⚡️-Tiny
 
-Pre-release of Chronos⚡️ (read: Chronos-Bolt) pretrained time series forecasting models. Chronos⚡️ models are based on the [T5 architecture](https://arxiv.org/abs/1910.10683) and are available in the following sizes.
+Chronos⚡️ (read: Chronos-Bolt) is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. Chronos⚡️ models are based on the [T5 architecture](https://arxiv.org/abs/1910.10683) and are available in the following sizes.
 
 
 <div align="center">
@@ -29,10 +29,14 @@ Pre-release of Chronos⚡️ (read: Chronos-Bolt) pretrained time series forecas
 
 ## Usage
 
-A minimal example showing how to perform inference using Chronos⚡️ with AutoGluon:
+> [!WARNING]  
+> Chronos⚡️ models will be available in the next stable release of AutoGluon, so the following instructions will only work once AutoGluon 1.2 has been released.
+
+
+A minimal example showing how to perform zero-shot inference using Chronos⚡️ with AutoGluon:
 
 ```
-pip install --pre autogluon
+pip install autogluon
 ```
 
 ```python
@@ -40,16 +44,14 @@ from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame
 
 df = TimeSeriesDataFrame("https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly/train.csv")
 
-predictions = TimeSeriesPredictor().fit(
+predictor = TimeSeriesPredictor(prediction_length=48).fit(
     df,
     hyperparameters={
-        "Chronos": [
-            {"model_path": "autogluon/chronos-bolt-tiny"},
-        ]
+        "Chronos": {"model_path": "autogluon/chronos-bolt-tiny"},
     },
-).predict(
-    df
 )
+
+predictions = predictor.predict(df)
 ```
 
 ## Citation