Scaling Laws for Language Models on Symbolic Music Data View Code Live Demo Investigated neural scaling laws on symbolic music by training 9 decoder-only Transformer and LSTM models (844K–201M parameters) on 100M+ tokens of ABC notation from 54K+ folk tunes