Instructions to use Bgeorge/model_dialect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bgeorge/model_dialect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Bgeorge/model_dialect")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Bgeorge/model_dialect") model = AutoModelForAudioClassification.from_pretrained("Bgeorge/model_dialect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Bgeorge/model_dialect: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Bgeorge/model_dialect/resolve/d8eb4b3c0baa542ba21487e3144f650bb34fded7/training_args.bin
- Command line
-
hf download hf://Bgeorge/model_dialect@d8eb4b3c0baa542ba21487e3144f650bb34fded7/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Bgeorge/model_dialect/resolve/d8eb4b3c0baa542ba21487e3144f650bb34fded7/training_args.bin
5.3 kB
- Xet hash:
- 9632f64c4cd8922ddf726c7a759035cf17049333579a41b71cd88bad567ba6be
- Size of remote file:
- 5.3 kB
- SHA256:
- 8404c263c32b063dcbd15fac95965d16627ee384aa04245bfb3fbde91a0b2228
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.