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--- |
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language: |
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- en |
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tags: |
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- NLP |
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license: mit |
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datasets: |
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- TristanBehrens/bach_garland_2024-100K |
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base_model: None |
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--- |
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# Bach Garland xLSTM - An xLSTM model trained on Johann Sebastian Bach Style music |
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Say Hello on [LinkedIn](https://www.linkedin.com/in/dr-tristan-behrens-734967a2/) and [X](https://x.com/DrTBehrens). |
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This is a xLSTM model trained on music by Johann Sebastian Bach. It includes all pieces of Bach's music that can be played on church organ. The samples come in the prototypical Garland notation. |
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The dataset contains 100K samples and comes with a total token count of 144M. |
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## How to use |
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1. Clone this repository and follow the installation instructions: https://github.com/AI-Guru/helibrunna/ |
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2. Open and run the notebook `examples/music.ipynb`. Do not forget to add the id of this model. |
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3. Enjoy! |
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## Training |
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Trained with [Helibrunna](https://github.com/AI-Guru/helibrunna) by [Dr. Tristan Behrens](https://de.linkedin.com/in/dr-tristan-behrens-734967a2). |
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## Configuration |
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``` |
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training: |
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model_name: bach_garland_xlstm |
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batch_size: 4 |
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lr: 0.001 |
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lr_warmup_steps: 5000 |
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lr_decay_until_steps: 50000 |
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lr_decay_factor: 0.001 |
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weight_decay: 0.1 |
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amp_precision: bfloat16 |
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weight_precision: float32 |
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enable_mixed_precision: true |
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num_epochs: 4 |
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output_dir: output/bach_garland_xlstm |
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save_every_step: 500 |
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log_every_step: 10 |
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wandb_project: bach_garland_xlstm |
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torch_compile: false |
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model: |
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num_blocks: 4 |
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embedding_dim: 64 |
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mlstm_block: |
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mlstm: |
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num_heads: 4 |
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slstm_block: |
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slstm: |
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num_heads: 4 |
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slstm_at: |
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- 2 |
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context_length: 4096 |
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vocab_size: 178 |
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dataset: |
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hugging_face_id: TristanBehrens/bach_garland_2024-100K |
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tokenizer: |
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type: whitespace |
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fill_token: '[EOS]' |
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``` |
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