Instructions to use Sjdan/torgo_mix_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/torgo_mix_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/torgo_mix_4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/torgo_mix_4") model = AutoModelForCTC.from_pretrained("Sjdan/torgo_mix_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/torgo_mix_4: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/torgo_mix_4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/torgo_mix_4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/torgo_mix_4/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 1384083968c1b3fa0dab1854b57f3d32ce4a8ed45403c72ff4af26e209a978f2
- Size of remote file:
- 1.26 GB
- SHA256:
- 360f6110f8f276b4c5c538751fb887ffd4b07181909a39611d4a94f7581a8ba5
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