add example, fix table

README.md

@@ -26,13 +26,53 @@ Chronos-T5 uses 4096 different tokens, compared to 32128 of the original T5 mode
  Model          | Parameters        | Based on
 ----------------|-------------------|----------------------
 [chronos-t5-mini](https://huggingface.co/amazon/chronos-t5-mini) | 20M | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini)
-[chronos-t5-small](https://huggingface.co/amazon/chronos-t5-small) | 46M | [flan-t5-small](https://huggingface.co/google/flan-t5-small)
-[chronos-t5-base](https://huggingface.co/amazon/chronos-t5-base) | 200M | [flan-t5-base](https://huggingface.co/google/flan-t5-base)
-[chronos-t5-large](https://huggingface.co/amazon/chronos-t5-large) | 710M | [flan-t5-large](https://huggingface.co/google/flan-t5-large)
+[chronos-t5-small](https://huggingface.co/amazon/chronos-t5-small) | 46M | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small)
+[chronos-t5-base](https://huggingface.co/amazon/chronos-t5-base) | 200M | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base)
+[chronos-t5-large](https://huggingface.co/amazon/chronos-t5-large) | 710M | [t5-efficient-large](https://huggingface.co/google/t5-efficient-large)
 
 ## Usage
 
-To do inference with Chronos models, refer to the code and examples in the [companion GitHub repo](https://www.example.com/).
+To do inference with Chronos models, you will need to install the code from the [companion GitHub repo](https://www.example.com/).
+
+```bash
+pip install git+https://github.com/amazon-science/chronos-forecasting.git
+```
+
+A minimal example:
+
+```python
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import torch
+from chronos import ChronosPipeline
+
+pipeline = ChronosPipeline.from_pretrained("amazon/chronos-t5-base")
+
+df = pd.read_csv(
+    "https://raw.githubusercontent.com/AileenNielsen/"
+    "TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv",
+    index_col=0,
+    parse_dates=True,
+)
+
+context = torch.Tensor(df["#Passengers"].values)
+forecast = pipeline.predict(context, prediction_length=12)
+
+forecast_steps = range(len(df), len(df) + 12)
+forecast_np = forecast.numpy()[0].T
+low = np.quantile(forecast_np, 0.1, axis=1)
+median = np.quantile(forecast_np, 0.5, axis=1)
+high = np.quantile(forecast_np, 0.9, axis=1)
+
+plt.plot(range(len(df)), df["#Passengers"], color="royalblue", label="historical data")
+plt.plot(forecast_steps, forecast_np, color="grey", alpha=0.1)
+plt.fill_between(forecast_steps, low, high, color="tomato", alpha=0.4, label="80% interval")
+plt.plot(forecast_steps, median, color="tomato", label="median")
+plt.legend()
+plt.grid()
+plt.show()
+```
 
 ## References