diff --git a/README.md b/README.md
index 54b11ae468..ad96afee8c 100644
--- a/README.md
+++ b/README.md
@@ -58,6 +58,12 @@ cd <example-folder>
 pip install -r requirements.txt
 ```
 
+> To use the example associated with the latest stable release, run:
+> ```
+> git checkout v1.10.1
+> ```
+> with `v1.10.1` the version number of this release.
+
 
 ## How to use it?
 
diff --git a/examples/audio-classification/README.md b/examples/audio-classification/README.md
index 071e7c7b58..58af855758 100644
--- a/examples/audio-classification/README.md
+++ b/examples/audio-classification/README.md
@@ -20,6 +20,7 @@ The following examples showcase how to fine-tune `Wav2Vec2` for audio classifica
 
 Speech recognition models that have been pretrained in an unsupervised fashion on audio data alone, *e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), have shown to require only very little annotated data to yield good performance on speech classification datasets.
 
+
 ## Single-HPU
 
 The following command shows how to fine-tune [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the 🗣️ [Keyword Spotting subset](https://huggingface.co/datasets/superb#ks) of the SUPERB dataset on a single HPU.