Instructions to use SHENMU007/neunit_BASE_V5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SHENMU007/neunit_BASE_V5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="SHENMU007/neunit_BASE_V5")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("SHENMU007/neunit_BASE_V5") model = AutoModelForTextToSpectrogram.from_pretrained("SHENMU007/neunit_BASE_V5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SHENMU007/neunit_BASE_V5: direct link, hf CLI and curl.
- Browser
- Download file 585 MB
-
https://huggingface.co/SHENMU007/neunit_BASE_V5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SHENMU007/neunit_BASE_V5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SHENMU007/neunit_BASE_V5/resolve/main/pytorch_model.bin
585 MB
- Xet hash:
- 72a03d33ac83eeb69bd97cdac74c31af58f10653fdb264c01ed6d582601481b2
- Size of remote file:
- 585 MB
- SHA256:
- 1ca59bc8175b804e8d30685521fa2ab40284187e9189d713d8b109cc418b9834
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.