Instructions to use Amik-ML/model_name with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amik-ML/model_name with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Amik-ML/model_name")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Amik-ML/model_name") model = AutoModelForAudioClassification.from_pretrained("Amik-ML/model_name", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Amik-ML/model_name: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/Amik-ML/model_name/resolve/main/training_args.bin
- Command line
-
hf download hf://Amik-ML/model_name/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Amik-ML/model_name/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 8b21538cc4f292770363da84fcddf40b79c5c0acbe7dacdf05f538b88f2ed3e4
- Size of remote file:
- 4.6 kB
- SHA256:
- 8b99850897790aad5591309143798b640d61a5a0be7315119a5fc323a2c98cd2
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