Instructions to use breadlicker45/Musenet-1B4-L96-D1024-2000-converted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use breadlicker45/Musenet-1B4-L96-D1024-2000-converted with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("breadlicker45/Musenet-1B4-L96-D1024-2000-converted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 303d385bef3e0e481ddfa42b3085145c4281fa17bfc8373158ab14d111bdc128
- Size of remote file:
- 5.65 GB
- SHA256:
- 29b8cfbd40fd7813c7454310f724fc31fe2c44ea5249d85aa352b885385c68a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.