Instructions to use talargv/musicgen-punk-sanity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use talargv/musicgen-punk-sanity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="talargv/musicgen-punk-sanity")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("talargv/musicgen-punk-sanity") model = AutoModelForTextToWaveform.from_pretrained("talargv/musicgen-punk-sanity") - Notebooks
- Google Colab
- Kaggle
musicgen-punk-sanity
This model is a fine-tuned version of facebook/musicgen-small on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.99) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
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Model tree for talargv/musicgen-punk-sanity
Base model
facebook/musicgen-small