diff --git a/README.md b/README.md
index e4d441b..a110457 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ See [Docker image](docs/docker-image.md) for details.
 - [Model construction](docs/model-construction.md)
 - [Pretrained models](docs/pretrained-models.md)
 - [Training examples](docs/training-examples.md)
-- [Inference examples](examples/inference.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/perceiver-io/blob/main/examples/inference.ipynb)
+- [Inference examples](examples/inference.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/perceiver-io/blob/0.7.0/examples/inference.ipynb)
 - [Building blocks](docs/building-blocks.md)
 
 ## Getting started
@@ -227,7 +227,7 @@ torch.save(model.state_dict(), "/path/to/model.pt")
 
 For generating text from a prompt via top-k sampling, `CausalLanguageModel` provides a `generate()` method. The following
 example first loads a trained model from a checkpoint and then generates text from a short sample prompt. An interactive
-demo is also available in the [Colab notebook](https://colab.research.google.com/github/krasserm/perceiver-io/blob/main/examples/inference.ipynb).
+demo is also available in the [Colab notebook](https://colab.research.google.com/github/krasserm/perceiver-io/blob/0.7.0/examples/inference.ipynb).
 
 ```python
 from perceiver.data.text import TextPreprocessor