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---
license: mit
library_name: transformers
tags:
- music-generation
- symbolic-music
- abc-notation
- quantized
- pytorch
base_model: sander-wood/notagen
pipeline_tag: text-generation
---
# NotaGenX-Quantized
This is a quantized version of the NotaGen model for symbolic music generation. The model generates music in ABC notation format and has been optimized for faster inference and reduced memory usage.
## Model Description
- **Base Model**: [sander-wood/notagen](https://huggingface.co/sander-wood/notagen)
- **Quantization**: INT8 dynamic quantization using PyTorch
- **Size Reduction**: ~75% smaller than the original model
- **Performance**: Faster inference with minimal quality loss
- **Memory**: Reduced VRAM requirements
## Model Architecture
- **Type**: GPT-2 based transformer for symbolic music generation
- **Input**: Period, Composer, Instrumentation prompts
- **Output**: ABC notation music scores
- **Patch Size**: 16
- **Patch Length**: 1024
- **Hidden Size**: 1280
- **Layers**: 20 (encoder) + 6 (decoder)
## Usage
```python
from weavemuse.tools.notagen_tool import NotaGenTool
# Initialize the tool (will automatically use quantized model)
notagen = NotaGenTool()
# Generate music
result = notagen("Classical", "Mozart", "Piano")
print(result["abc"])
```
## Quantization Details
This model has been quantized using PyTorch's dynamic quantization:
- **Method**: Dynamic INT8 quantization
- **Target**: Linear and embedding layers
- **Preserved**: Model architecture and functionality
- **Testing**: Validated against original model outputs
## Performance Comparison
| Metric | Original | Quantized | Improvement |
|--------|----------|-----------|-------------|
| Model Size | ~2.3GB | ~0.6GB | 75% reduction |
| Load Time | ~15s | ~4s | 73% faster |
| Inference | Baseline | 1.2-1.5x faster | 20-50% speedup |
| VRAM Usage | ~2.1GB | ~0.8GB | 62% reduction |
## Installation
```bash
pip install weavemuse
```
## Citation
If you use this model, please cite the original NotaGen paper:
```bibtex
@article{notagen2024,
title={NotaGen: Symbolic Music Generation with Fine-Grained Control},
author={Wood, Sander and others},
year={2024}
}
```
## License
MIT License - see the original model repository for full license details.
## Contact
- **Maintainer**: manoskary
- **Repository**: [weavemuse](https://github.com/manoskary/weavemuse)
- **Issues**: Please report issues on the main repository
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