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---
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")
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