diff --git a/website/docs/usage/layers-architectures.md b/website/docs/usage/layers-architectures.md
index eb46dd137..f9f3e81a0 100644
--- a/website/docs/usage/layers-architectures.md
+++ b/website/docs/usage/layers-architectures.md
@@ -281,14 +281,70 @@ the transformer layers, and use "native" Thinc layers to do fiddly input and
 output transformations and add on task-specific "heads", as efficiency is less
 of a consideration for those parts of the network.
 
-<!-- TODO: custom tagger implemented in PyTorch, wrapped as Thinc model, link off to project (with notebook?) -->
-
-## Implementing models in Thinc {#thinc}
-
-<!-- TODO: use same example as above, custom tagger, but implemented in Thinc, link off to Thinc docs where appropriate -->
 
 ## Models for trainable components {#components}
 
+To use our custom model including the Pytorch subnetwork, all we need to do is register 
+the architecture. The full example then becomes:
+
+```python
+from typing import List
+from thinc.types import Floats2d
+from thinc.api import Model, PyTorchWrapper, chain, with_array
+import spacy
+from spacy.tokens.doc import Doc
+from spacy.ml import CharacterEmbed
+from torch import nn
+
+@spacy.registry.architectures("CustomTorchModel.v1")
+def TorchModel(nO: int,
+    width: int,
+    hidden_width: int,
+    embed_size: int,
+    nM: int,
+    nC: int,
+    dropout: float,
+) -> Model[List[Doc], List[Floats2d]]:
+    embed = CharacterEmbed(width, embed_size, nM, nC)
+    torch_model = nn.Sequential(
+        nn.Linear(width, hidden_width),
+        nn.ReLU(),
+        nn.Dropout2d(dropout),
+        nn.Linear(hidden_width, nO),
+        nn.ReLU(),
+        nn.Dropout2d(dropout),
+        nn.Softmax(dim=1)
+    )
+    wrapped_pt_model = PyTorchWrapper(torch_model)
+    model = chain(embed, with_array(wrapped_pt_model))
+    return model
+```
+
+Now you can use this model definition in any existing trainable spaCy component, 
+by specifying it in the config file:
+
+```ini
+### config.cfg (excerpt) {highlight="6-6"}
+[components.tagger]
+factory = "tagger"
+
+[components.tagger.model]
+@architectures = "CustomTorchModel.v1"
+nO = 50
+nM = 64
+nC = 8
+dropout = 0.2
+width = 96
+hidden_width = 48
+embed_size = 2000
+```
+
+In this configuration, we pass all required parameters for the various 
+subcomponents of the custom architecture as settings in the training config file.
+Remember that it is best not to rely on any (hidden) default values, to ensure that 
+training configs are complete and experiments fully reproducible.
+
+
 <!-- TODO:
 
 - Interaction with `predict`, `get_loss` and `set_annotations`