Instructions to use walton98/model_out3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use walton98/model_out3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="walton98/model_out3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("walton98/model_out3") model = AutoModelForAudioClassification.from_pretrained("walton98/model_out3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from walton98/model_out3: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/walton98/model_out3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://walton98/model_out3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/walton98/model_out3/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- d5e4317da3c120540cd7c59f42cea9760df997c3f8ab6dcafa86d1af46911d80
- Size of remote file:
- 378 MB
- SHA256:
- 1b5922e40e1183a3ec0f2d2c37a14db9cba5ae3b4e694255093a08b5b02718b9
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