Decomposer
Decomposer decompiles symbolic music (MIDI) into executable Strudel programs. This model is the 8B model from the paper Decomposer: Learning to Decompile Symbolic Music to Programs, post-trained from Qwen3-8B.
- 🎹 Live demo: anonymousgiraffe/decomposer-demo
- 🤗 Dataset: anonymousgiraffe/Strudel-Synth
Model Description
- Base model: Qwen/Qwen3-8B
- Model size: 8.2B
- Type: Causal Language Model
- Precision: bfloat16
Model Details
Input Format
MIDI is serialized as instrument-wise note events. Onsets are given in cycle coordinates instead of seconds:
[BPM=120 Meter=4]
[acoustic_grand_piano] C4@0.00 E4@0.25 G4@0.50 C5@1.00
[drum] bass_drum@0.00 closed_hi_hat@0.25 closed_hi_hat@0.50 snare@0.75
- Header
[BPM=… Meter=…]— tempo/meter; if absent, madmom is used to estimate them. - Events — one line per instrument,
pitch@cycle_onsettokens; melodic pitches as note names (C4,F#3), drums as GM drum names (bass_drum,closed_hi_hat).
Training Data
Strudel-Synth is a synthetic corpus of 21,174 (MIDI, Strudel) pairs (20,152 train / 1,022 test) built by distilling Strudel programs from Claude-Opus-4.6 and rendering each to MIDI with the Strudel runtime. Its ~20K training split is divided into two disjoint ~10K halves, one per stage:
- SFT uses the first half as paired (MIDI, Strudel) examples.
- RL uses the second half (MIDI only), plus short-fragment (<30 s) LMD MIDI.
Inference Hyperparameters
Recommended settings matching the paper evaluation:
| Setting | Value |
|---|---|
temperature | 1.0 |
max_new_tokens | 4096 |
enable_thinking | false |