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