Instructions to use nour-world/test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nour-world/test-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("nour-world/test-model") model = AutoModelForAudioFrameClassification.from_pretrained("nour-world/test-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nour-world/test-model: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/nour-world/test-model/resolve/main/training_args.bin
- Command line
-
hf download hf://nour-world/test-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nour-world/test-model/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 0fd7992c36a7ff0e84fc960fb980c265f67bde15b88081858a354c7b3bed075a
- Size of remote file:
- 5.3 kB
- SHA256:
- 8a42eea92c281f5838a526b8ffa9b9ebe046d39e16ced7bc947e952631080907
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.