Instructions to use sixcarben/test_model_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sixcarben/test_model_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="sixcarben/test_model_dir")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("sixcarben/test_model_dir") model = AutoModelForAudioClassification.from_pretrained("sixcarben/test_model_dir", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sixcarben/test_model_dir: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/sixcarben/test_model_dir/resolve/main/training_args.bin
- Command line
-
hf download hf://sixcarben/test_model_dir/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sixcarben/test_model_dir/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- b106d9ffa3130c7e520eddad7a2fb9c7a4299a0a85d11ac306343dd9de7c7b03
- Size of remote file:
- 5.18 kB
- SHA256:
- b45d6929fa195f0b0b80c4fd224c7c9b7ef8cb332814fc7f58587770c45de73f
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