Instructions to use Selma323/first_full_dys_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Selma323/first_full_dys_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Selma323/first_full_dys_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Selma323/first_full_dys_model") model = AutoModelForAudioClassification.from_pretrained("Selma323/first_full_dys_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Selma323/first_full_dys_model: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/Selma323/first_full_dys_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Selma323/first_full_dys_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Selma323/first_full_dys_model/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- c488ae01a2c1657c5c9c6f5c6fa2c39efcb3ea392adb952f4c947216667973d3
- Size of remote file:
- 4.98 kB
- SHA256:
- 22dd4b5bc1ede46f5010b775057ac151191a69ad53a80be45cb7b5b238568242
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.