AEmotionStudio/midi-llm-models

MIDI-LLM — MAESTRO mirror

Built with Llama.

Verbatim mirror of `slseanwu/MIDI-LLM_Llama-3.2-1B` (revision 8b82ab9e) for the MAESTRO audio workstation's MIDI-LLM panel — text → multi-instrument General-MIDI generation, landing in the piano roll as one pattern per instrument.

MIDI-LLM (Shih-Lun Wu et al., ISMIR 2026 — code at slSeanWU/MIDI-LLM) is Llama-3.2-1B whose vocabulary is extended with the 55 030 Anticipatory Music Transformer MIDI tokens (onset time, duration, instrument-pitch triplets at 10 ms resolution). It was continued-pretrained on MusicPile + GigaMIDI and fine-tuned on Lakh MIDI paired with MidiCaps captions.

Files

Nothing is converted or quantised. MAESTRO loads the checkpoint with plain transformers (AutoModelForCausalLM, bf16, no remote code) via backend/ai/models/midi_llm.py and decodes the MIDI tokens with an in-tree tolerant parser (backend/ai/midi_llm/), so no anticipation install is needed.

Prompting

Inference follows upstream generate_transformers.py: the text "You are a world-class composer. Please compose some music according to the following description: <your text> " is tokenized, the MIDI begin token (128256 + 55026) is appended, and the model samples MIDI tokens (temperature 1.0, top-p 0.98, up to 2 046 new tokens ≈ 680 notes). Describe the music the way MidiCaps captions read — genre, mood, instruments by General-MIDI name, key, time signature, tempo feel, a chord progression.

License

The weights are subject to the [Llama 3.2 Community License Agreement](https://www.llama.com/llama3_2/license/) (a copy ships here as LICENSE.md). Redistribution and products built on it must display "Built with Llama" and include the license; commercial use is permitted below the 700 M monthly-active-user threshold; the Acceptable Use Policy applies. This mirror adds nothing of its own and claims no additional rights.

The tokenization scheme is the Anticipatory Music Transformer (John Thickstun et al., Apache-2.0).

Citation

@inproceedings{wu2026midillm,
  title     = {MIDI-LLM: Adapting Large Language Models for Text-to-MIDI Music Generation},
  author    = {Wu, Shih-Lun and others},
  booktitle = {Proceedings of the International Society for Music Information Retrieval Conference (ISMIR)},
  year      = {2026}
}