Instructions to use midoiv/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use midoiv/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="midoiv/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("midoiv/results") model = AutoModelForAudioClassification.from_pretrained("midoiv/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from midoiv/results: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/midoiv/results/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://midoiv/results/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/midoiv/results/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 0adb64cd68e43278e2be9e2cbfdfc7a7107ffebf56422cdaafab0d24a5729a42
- Size of remote file:
- 1.26 GB
- SHA256:
- 97afddbdc8797269658fae9b166806b2317cfd6e3f3e1746e1d489b859311d1b
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