Update README

README.md

@@ -10,16 +10,33 @@ tags:
   - time-series
 ---
 
-# Chronos-Bolt
+# Chronos⚡️-Base
+
+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">
+
+| Model                                                                  | Parameters | Based on                                                               |
+| ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- |
+| [**chronos-bolt-tiny**](https://huggingface.co/autogluon/chronos-bolt-tiny)   | 9M         | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny)   |
+| [**chronos-bolt-mini**](https://huggingface.co/autogluon/chronos-bolt-mini)   | 21M        | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini)   |
+| [**chronos-bolt-small**](https://huggingface.co/autogluon/chronos-bolt-small) | 48M        | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) |
+| [**chronos-bolt-base**](https://huggingface.co/autogluon/chronos-bolt-base)   | 205M       | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base)   |
+
+</div>
 
-Pre-release of Chronos-Bolt pretrained time series forecasting models. 
 
 ## Usage
 
-A minimal example showing how to perform inference using Chronos-Bolt 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
@@ -27,21 +44,19 @@ 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-base"},
-        ]
+        "Chronos": {"model_path": "autogluon/chronos-bolt-base"},
     },
-).predict(
-    df
 )
+
+predictions = predictor.predict(df)
 ```
 
 ## Citation
 
-If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815):
+If you find Chronos or Chronos⚡️ models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815):
 
 ```
 @article{ansari2024chronos,