Instructions to use tarsssss/my_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarsssss/my_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="tarsssss/my_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("tarsssss/my_model") model = AutoModelForAudioClassification.from_pretrained("tarsssss/my_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tarsssss/my_model: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/tarsssss/my_model/resolve/main/training_args.bin
- Command line
-
hf download hf://tarsssss/my_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tarsssss/my_model/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- c8e3a570b67823068b67cb524e757c1409549d5dfb89c34f14afd0465b89aa56
- Size of remote file:
- 4.98 kB
- SHA256:
- a1afb10c4203f9243c1967618ad8146c6e1d79e10b42db4c295ca95c1c5ad84b
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