Replace with clean markdown card

README.md

@@ -15,11 +15,10 @@ tags:
 
 Deprecated alias for EEGNet.
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.EEGNetv4` class. **No pretrained weights are
-> distributed here** — instantiate the model and train it on your own
-> data, or fine-tune from a published foundation-model checkpoint
-> separately.
+> distributed here.** Instantiate the model and train it on your own
+> data.
 
 ## Quick start
 
@@ -38,46 +37,26 @@ model = EEGNetv4(
 )
 ```
 
-The signal-shape arguments above are example defaults — adjust them
-to match your recording.
+The signal-shape arguments above are illustrative defaults — adjust to
+match your recording.
 
 ## Documentation
-
-- Full API reference (parameters, references, architecture figure):
-  <https://braindecode.org/stable/generated/braindecode.models.EEGNetv4.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.EEGNetv4.html>
+- Interactive browser (live instantiation, parameter counts):
   <https://huggingface.co/spaces/braindecode/model-explorer>
 - Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/eegnet.py#L353>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>Deprecated alias for EEGNet.</p>
-<blockquote>
-<p class="rubric">Hugging Face Hub integration</p>
-<p>When the optional <span class="docutils literal">huggingface_hub</span> package is installed, all models
-automatically gain the ability to be pushed to and loaded from the
-Hugging Face Hub. Install with:</p>
-<pre class="literal-block">pip install braindecode[hub]</pre>
-<p><strong>Pushing a model to the Hub:</strong></p>
-<p><strong>Loading a model from the Hub:</strong></p>
-<p><strong>Extracting features and replacing the head:</strong></p>
-<p><strong>Saving and restoring full configuration:</strong></p>
-<p>All model parameters (both EEG-specific and model-specific such as
-dropout rates, activation functions, number of filters) are automatically
-saved to the Hub and restored when loading.</p>
-<p>See :ref:`load-pretrained-models` for a complete tutorial.</p>
-</blockquote>
-</main>
-</div>
+
+
+## Parameters
+
+_(see source for parameter list)_
+
+
 
 ## Citation
 
-Please cite both the original paper for this architecture (see the
-*References* section above) and braindecode:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,