diff --git a/README.md b/README.md
index 2c10b6bbf851073d49dc2b9542e11dd0418e304c..ee8ec5cca22a32dc242ea7b9bcb3a66446d523e0 100644
--- a/README.md
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@@ -2,6 +2,16 @@
 
 Python implementation of the 2L-VKOGA algorithm, which uses a kernel optimization (two layered kernel) before running VKOGA with the modified kernel.
 
+
+## Installation
+
+pip install git+https://gitlab.mathematik.uni-stuttgart.de/pub/ians-anm/2l-vkoga@v0.1.0
+
+
+## Usage
+
+Have a look into the [demo file](https://gitlab.mathematik.uni-stuttgart.de/pub/ians-anm/2l-vkoga/-/blob/main/example_files/01_testfile.py).
+
 ## How to cite:
 If you use this code in your work for scalar-valued output data, please cite the paper
 
diff --git a/example_files/01_testfile.py b/example_files/01_testfile.py
index b8cecc02d657ed5510a416a63869a975e7ef1a70..9e740614b056c879ef659118c26c208af3c37685 100644
--- a/example_files/01_testfile.py
+++ b/example_files/01_testfile.py
@@ -41,26 +41,6 @@ plt.draw()
 
 
 
-# Result: Since the training set is quite small, we can clearly observe overfitting via the
-# validation set tracking
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