Update README.md

README.md

@@ -25,7 +25,59 @@ You are free to use this model in any open-source context without charge. Howeve
 
 ## Model description
 
-The model is GPT-2 with 6 decoders and 8 attention-heads each. The context length is 2048. The embedding dimensions are 512 as well.
+The model is GPT-2 with 6 decoders and 8 attention heads each. The context length is 2048. The embedding dimensions are 512.
+
+## Model family
+
+This model is part of a huge group of Transformers I have trained. Most of them are not publicly available.
+
+If you are interested in using andor licensing one of the models, please get in touch. 
+
+### Lakhclean
+
+These models were trained on roundabout 15K MIDI files (the same as the model you are viewing now) from the Lakhclean dataset.
+
+- lakhclean_mmmbar_4bars_d-2048: 4 bars resolution, bar inpainting, note density conditioning
+- lakhclean_mmmbar_8bars_d-2048: 8 bars resolution, bar inpainting, note density conditioning
+- lakhclean_mmmtrack_4bars_chords: 4 bars resolution, chord conditioning
+- lakhclean_mmmtrack_4bars_d-2048: 4 bars resolution, note density conditioning (this model)
+- lakhclean_mmmtrack_4bars_simple-2048: 4 bars resolution
+- lakhclean_mmmtrack_8bars_d-2048: 8 bars resolution, note density conditioning
+
+### Lakhfull
+
+These models were trained on roundabout 175K MIDI files from the Lakh dataset.
+
+- lakhfull_mmmtrack_4bars_d-2048: 4 bars resolution, note density conditioning (the big brother of this model)
+- lakhfull_mmmtrack_4bars_simple-2048: 4 bars resolution
+
+### Metal
+
+These models were trained on roundabout 7K MIDI files from my own collections. They contain genre conditioning.
+
+- metal_mmmbar_4bars_d-2048: 4 bars resolution, bar inpainting, note density conditioning
+- metal_mmmbar_8bars_d-2048: 8 bars resolution, bar inpainting, note density conditioning
+- metal_mmmtrack_4bars_d-2048: 4 bars resolution, note density conditioning
+- metal_mmmtrack_8bars_d-2048: 8 bars resolution, note density conditioning
+
+### MetaMIDI Dataset genres
+
+These models were trained on genre-specific subsets of the MetaMIDI dataset.
+
+- mmd-baroque_mmmtrack_4bars_d-2048: 4 bars resolution, note density conditioning
+- mmd-baroque_mmmtrack_8bars_d-2048: 8 bars resolution, note density conditioning
+- mmd-classical_mmmtrack_8bars_d-2048: 8 bars resolution, note density conditioning
+- mmd-noncontemporary_mmmtrack_8bars_d-2048: 8 bars resolution, note density conditioning
+- mmd-pop_mmmtrack_8bars_d-2048: 8 bars resolution, note density conditioning
+- mmd-renaissance_mmmtrack_8bars_d-2048: 8 bars resolution, note density conditioning
+
+### MetaMIDI Dataset full
+
+These models were trained on roundabout 400K MIDI files from the MetaMIDI dataset.
+
+- mmd-full_mmmtrack_4bars_d-2048: 4 bars resolution, note density conditioning
+- mmd-full_mmmtrack_8bars_d-2048: 8 bars resolution, note density conditioning
+- mmd-full_mmmtrack_4bars_chords-d-2048: 8 bars resolution, note density conditioning, chord conditioning (most powerful model in the entire group)
 
 ## Intended uses & limitations