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/5ed43c2114cdb744dde7bedefbbbdfd94b479c65/pytorch_model.bin
- Command line
-
hf download hf://anderloh/HuggingfaceTest@5ed43c2114cdb744dde7bedefbbbdfd94b479c65/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anderloh/HuggingfaceTest/resolve/5ed43c2114cdb744dde7bedefbbbdfd94b479c65/pytorch_model.bin
52.2 MB
- Xet hash:
- 61193c6570e27131edfc0a14b98d28a21c34bdd00bc0dbed4eaac69a17c63197
- Size of remote file:
- 52.2 MB
- SHA256:
- e5fd8245595ad7fe1d3fdf171cad01ec66fd98baf68ba18e05b7868b9bd40bcf
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