Instructions to use JovialValley/model_broadclass_onSet0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JovialValley/model_broadclass_onSet0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JovialValley/model_broadclass_onSet0")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("JovialValley/model_broadclass_onSet0") model = AutoModelForCTC.from_pretrained("JovialValley/model_broadclass_onSet0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JovialValley/model_broadclass_onSet0: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/JovialValley/model_broadclass_onSet0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JovialValley/model_broadclass_onSet0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JovialValley/model_broadclass_onSet0/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 6b96c35b4aa53c1434ca9128599c4d98605ff7ece63965ba3dc1f5a169552298
- Size of remote file:
- 1.26 GB
- SHA256:
- 9c41a00f9eca3c6529852ba9faf18863232f82aa760d9aac2535f0f43ed03df6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.