Instructions to use anderloh/HuggingfaceTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anderloh/HuggingfaceTest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anderloh/HuggingfaceTest")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anderloh/HuggingfaceTest") model = AutoModelForAudioClassification.from_pretrained("anderloh/HuggingfaceTest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anderloh/HuggingfaceTest: direct link, hf CLI and curl.
- Browser
- Download file 52.2 MB
-
https://huggingface.co/anderloh/HuggingfaceTest/resolve/bc4292de658c32bda9f2caefb6cbbee22adcfae6/pytorch_model.bin
- Command line
-
hf download hf://anderloh/HuggingfaceTest@bc4292de658c32bda9f2caefb6cbbee22adcfae6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anderloh/HuggingfaceTest/resolve/bc4292de658c32bda9f2caefb6cbbee22adcfae6/pytorch_model.bin
52.2 MB
- Xet hash:
- e3a72be43ffe233411af7f68665341c799efca704c2285a91b2a2c497249cafa
- Size of remote file:
- 52.2 MB
- SHA256:
- a61ebff2c7b6e47a546319feefed62078eabe76f26b7cbc0c6ff25d72dfc87e4
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