Instructions to use JM/my_awesome_mind_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JM/my_awesome_mind_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="JM/my_awesome_mind_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("JM/my_awesome_mind_model") model = AutoModelForAudioClassification.from_pretrained("JM/my_awesome_mind_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JM/my_awesome_mind_model: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/JM/my_awesome_mind_model/resolve/main/training_args.bin
- Command line
-
hf download hf://JM/my_awesome_mind_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JM/my_awesome_mind_model/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- e04c5b7577936c04314ea146bf50c5cc3b2d08d251ff369029986a2a6600bc10
- Size of remote file:
- 3.58 kB
- SHA256:
- 7820aec8fc3eea69d347a32ba5c0ed76d49f52141e22be274075ceba6c0bfd45
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