Instructions to use SHENMU007/neunit_BASE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SHENMU007/neunit_BASE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="SHENMU007/neunit_BASE")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("SHENMU007/neunit_BASE") model = AutoModelForTextToSpectrogram.from_pretrained("SHENMU007/neunit_BASE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SHENMU007/neunit_BASE: direct link, hf CLI and curl.
- Browser
- Download file 585 MB
-
https://huggingface.co/SHENMU007/neunit_BASE/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://SHENMU007/neunit_BASE@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SHENMU007/neunit_BASE/resolve/refs%2Fpr%2F2/pytorch_model.bin
585 MB
- Xet hash:
- 2753d378e3f2149ddd908686d132a3d155d27e5b1168bf0ea15f9f624419864a
- Size of remote file:
- 585 MB
- SHA256:
- adf43d58b8d102a5bb96dcbfa5f81dcf12c1caf4b233bca7e944cea1e2039c80
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.