Instructions to use SHENMU007/neunit0424 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SHENMU007/neunit0424 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="SHENMU007/neunit0424")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("SHENMU007/neunit0424") model = AutoModelForTextToSpectrogram.from_pretrained("SHENMU007/neunit0424", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SHENMU007/neunit0424: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/SHENMU007/neunit0424/resolve/main/training_args.bin
- Command line
-
hf download hf://SHENMU007/neunit0424/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SHENMU007/neunit0424/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 94a526a2ec1c0d1b7a5cca9d4df07c9df65778deb4514cc5bc91f2944c1be0f9
- Size of remote file:
- 4.09 kB
- SHA256:
- 68d32e07dbcccab884be0f7f943b41ecc1c903408bbd9208eae83269864d3eba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.