diff --git a/docs/source/usage.rst b/docs/source/usage.rst
index 1fc0b13f..ac98b6b3 100644
--- a/docs/source/usage.rst
+++ b/docs/source/usage.rst
@@ -1315,97 +1315,4 @@ Below is the documentation on the available arguments.
                             Interval of validation in training
     --train-ratio 0.8     Ratio of train dataset. The remaining will be used for valid and test split.
     --valid-ratio 0.1     Ratio of validation set after the train data split. The remaining will be test split
-    --share-model
-
-Model initialization using the Torch API 
-----------------------------------------
-
-The scikit-learn API provides parametrization to many common use cases.
-The Torch API however allows for more flexibility and customization, for e.g. 
-sampling, criterions, and data loaders.
-
-In this minimal example we show how to initialize a CEBRA model using the Torch API.
-Here the :py:class:`cebra.data.single_session.DiscreteDataLoader` 
-gets initilized which also allows the `prior` to be directly parametrized.
-
-👉 For an example notebook using the Torch API check out the :doc:`demo_notebooks/Demo_Allen`.
-
-
-.. testcode::
-
-    import numpy as np
-    import cebra.datasets
-    from cebra import plot_embedding
-    import torch
-
-    if torch.cuda.is_available():
-        device = "cuda"
-    else:
-        device = "cpu"
-
-    neural_data = cebra.load_data(file="neural_data.npz", key="neural")
-
-    discrete_label = cebra.load_data(
-        file="auxiliary_behavior_data.h5", key="auxiliary_variables", columns=["discrete"],
-    )
-
-    # 1. Define Cebra Dataset
-    InputData = cebra.data.TensorDataset(
-        torch.from_numpy(neural_data).type(torch.FloatTensor),
-        discrete=torch.from_numpy(np.array(discrete_label[:, 0])).type(torch.LongTensor),
-    ).to(device)
-
-    # 2. Define Cebra Model
-    neural_model = cebra.models.init(
-        name="offset10-model",
-        num_neurons=InputData.input_dimension,
-        num_units=32,
-        num_output=2,
-    ).to(device)
-
-    InputData.configure_for(neural_model)
-
-    # 3. Define Loss Function Criterion and Optimizer
-    Crit = cebra.models.criterions.LearnableCosineInfoNCE(
-        temperature=0.001,
-        min_temperature=0.0001
-    ).to(device)
-
-    Opt = torch.optim.Adam(
-        list(neural_model.parameters()) + list(Crit.parameters()),
-        lr=0.001,
-        weight_decay=0,
-    )
-
-    # 4. Initialize Cebra Model
-    solver = cebra.solver.init(
-        name="single-session",
-        model=neural_model,
-        criterion=Crit,
-        optimizer=Opt,
-        tqdm_on=True,
-    ).to(device)
-
-    # 5. Define Data Loader
-    loader = cebra.data.single_session.DiscreteDataLoader(
-        dataset=InputData, num_steps=10, batch_size=200, prior="uniform"
-    )
-
-    # 6. Fit Model
-    solver.fit(loader=loader)
-
-    # 7. Transform Embedding
-    TrainBatches = np.lib.stride_tricks.sliding_window_view(
-        neural_data, neural_model.get_offset().__len__(), axis=0
-    )
-
-    X_train_emb = solver.transform(
-        torch.from_numpy(TrainBatches[:]).type(torch.FloatTensor).to(device)
-    ).to(device)
-
-    # 8. Plot Embedding
-    plot_embedding(
-        X_train_emb,
-        discrete_label[neural_model.get_offset().__len__() - 1 :, 0],
-        markersize=10,
-    )
+    --share-model
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