Create README.md

README.md

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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")