diff --git a/README.md b/README.md
index b82a181..96e6da2 100644
--- a/README.md
+++ b/README.md
@@ -68,18 +68,12 @@ pip install git+https://github.com/amazon-science/chronos-forecasting.git
 > [!TIP]  
 > The recommended way of using Chronos for production use cases is through [AutoGluon](https://auto.gluon.ai), which features ensembling with other statistical and machine learning models for time series forecasting as well as seamless deployments on AWS with SageMaker 🧠. Check out the AutoGluon Chronos [tutorial](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-chronos.html).
 
-> [!NOTE]  
-> We have added 🧪experimental support for [MLX](https://github.com/ml-explore/mlx) inference. If you have an Apple Silicon Mac, check out the [`mlx`](https://github.com/amazon-science/chronos-forecasting/tree/mlx) branch of this repository for instructions on how to install and use the MLX version of Chronos. 
-
 ### Forecasting
 
 A minimal example showing how to perform forecasting using Chronos models:
 
 ```python
-# for plotting, run: pip install pandas matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
+import pandas as pd  # requires: pip install pandas
 import torch
 from chronos import ChronosPipeline
 
@@ -93,19 +87,27 @@ df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnal
 
 # context must be either a 1D tensor, a list of 1D tensors,
 # or a left-padded 2D tensor with batch as the first dimension
-context = torch.tensor(df["#Passengers"])
-prediction_length = 12
+# forecast shape: [num_series, num_samples, prediction_length]
 forecast = pipeline.predict(
-    context,
-    prediction_length,
+    context=torch.tensor(df["#Passengers"]),
+    prediction_length=12,
     num_samples=20,
-    temperature=1.0,
-    top_k=50,
-    top_p=1.0,
-) # forecast shape: [num_series, num_samples, prediction_length]
+)
+```
 
-# visualize the forecast
-forecast_index = range(len(df), len(df) + prediction_length)
+More options for `pipeline.predict` can be found with:
+
+```python
+print(ChronosPipeline.predict.__doc__)
+```
+
+We can now visualize the forecast:
+
+```python
+import matplotlib.pyplot as plt  # requires: pip install matplotlib
+import numpy as np
+
+forecast_index = range(len(df), len(df) + 12)
 low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0)
 
 plt.figure(figsize=(8, 4))
@@ -142,7 +144,7 @@ embeddings, tokenizer_state = pipeline.embed(context)
 
 ### Pretraining and fine-tuning
 
-Scripts for pretraining and fine-tuning Chronos models can be found in [this folder](./scripts/training).
+Scripts for pretraining and fine-tuning Chronos models can be found in [this folder](./scripts/).
 
 ## 🔥 Coverage