Instructions to use Selma323/SynthLight-TorgoLight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Selma323/SynthLight-TorgoLight with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Selma323/SynthLight-TorgoLight")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Selma323/SynthLight-TorgoLight") model = AutoModelForAudioClassification.from_pretrained("Selma323/SynthLight-TorgoLight", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Selma323/SynthLight-TorgoLight: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/Selma323/SynthLight-TorgoLight/resolve/main/training_args.bin
- Command line
-
hf download hf://Selma323/SynthLight-TorgoLight/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Selma323/SynthLight-TorgoLight/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 38a67461552e54d05b9aa132bbb12573b53377f0cd3a5245e946e1d1ea01b825
- Size of remote file:
- 4.98 kB
- SHA256:
- 91b00d81d01a3cb1d2843df0cddd6dbf813d9bc523ac0d5af2529483d950335e
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