Instructions to use Rossi9/Model_train_dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rossi9/Model_train_dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Rossi9/Model_train_dev")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Rossi9/Model_train_dev") model = AutoModelForAudioClassification.from_pretrained("Rossi9/Model_train_dev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Rossi9/Model_train_dev: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/Rossi9/Model_train_dev/resolve/main/training_args.bin
- Command line
-
hf download hf://Rossi9/Model_train_dev/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Rossi9/Model_train_dev/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 79904a8b9bc1594bcfbfebb6fe00d2abe1621657ccf410cc0c6a6527cd69b5a3
- Size of remote file:
- 4.92 kB
- SHA256:
- de072e19a3dac0dcc3201c4c5e9532b5d2242a5058f865acc02f3c539f53760e
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