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 pytorch_model.bin from JM/my_awesome_mind_model: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/JM/my_awesome_mind_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JM/my_awesome_mind_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JM/my_awesome_mind_model/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 2fcfc4ff30f3ff3838150dde1e7492dfeccd7c10f40c82cb7ebadcfa6abb179e
- Size of remote file:
- 378 MB
- SHA256:
- c53a9042650161d503fad73348f0845daca621fe5f08c549940594b5eb67c468
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