---
pipeline_tag: MIDI or Chord to MIDI or Chord
tags:
- music-generation
- transformer
- MoE
- ALiBi
- FlashAttention
- melody-generation
- rhythmic-modeling
---
# Model Card for MORTM (Metric-Oriented Rhythmic Transformer for Melodic generation)

MORTM is a Transformer-based model designed for melody generation, with a strong emphasis on metric (rhythmic) structure. It represents music as sequences of pitch, duration, and relative beat positions within a measure (normalized to 96 ticks), making it suitable for time-robust, rhythm-aware music generation tasks.

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

MORTM (Metric-Oriented Rhythmic Transformer for Melodic generation) is a decoder-only Transformer architecture optimized for music generation with rhythmic awareness. It generates melodies measure-by-measure in an autoregressive fashion. The model supports chord-conditional generation and is equipped with the following features:

- Mixture of Experts (MoE) in the feedforward layers for capacity increase and compute efficiency.
- ALiBi (Attention with Linear Biases) for relative positional biasing.
- FlashAttention2 for fast and memory-efficient attention.
- Relative tick-based tokenization (e.g., [Position, Duration, Pitch]) for metric robustness.

- **Developed by:** Koue Okazaki & Takaki Nagoshi
- **Funded by [optional]:** Nihon University, Graduate School of Integrated Basic Sciences
- **Shared by [optional]:** ProjectMORTM
- **Model type:** Transformer (decoder-only with MoE and ALiBi)
- **Language(s) (NLP):** N/A (music domain)
- **License:** MIT
- **Finetuned from model [optional]:** Custom-built from scratch (not fine-tuned from a pretrained LM)

### Model Sources [optional]

- **Repository:** [https://github.com/Ayato964/MORTM](https://github.com/Ayato964/MORTM) *(replace with actual link)*
- **Paper [optional]:** In submission
- **Demo [optional]:** Coming soon

## Uses

### Direct Use

MORTM can generate melodies from scratch or conditionally based on chord progressions. It is ideal for:

- Melody composition in pop, jazz, and improvisational styles.
- Real-time melodic suggestion systems for human-AI co-creation.
- Music education and melody completion tools.

### Downstream Use [optional]

- Style transfer with different chord inputs.
- Harmonization and rhythm-based accompaniment systems.

### Out-of-Scope Use

- Audio-to-audio tasks (e.g., vocal separation).
- Raw audio synthesis (requires additional vocoder).
- Not suitable for genre classification or music recommendation.

## Bias, Risks, and Limitations

As the training dataset is primarily composed of Western tonal music, the model may underperform on:

- Non-tonal, microtonal, or traditional music styles.
- Polyrhythmic or tempo-variable music.
- Genres not sufficiently represented in training data (e.g., Indian classical).

### Recommendations

Generated melodies should be manually reviewed in professional music contexts. Users are encouraged to retrain or fine-tune on representative datasets when applying to culturally specific music.

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("nagoshidayo/mortm")
tokenizer = AutoTokenizer.from_pretrained("nagoshidayo/mortm")