Instructions to use Sjdan/torgo_85_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/torgo_85_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/torgo_85_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/torgo_85_2") model = AutoModelForCTC.from_pretrained("Sjdan/torgo_85_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/torgo_85_2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/torgo_85_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/torgo_85_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/torgo_85_2/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- bd74c1b79ce0d2177ec1a73bd435c03660ffe8baa8445145d64266d16cca97c4
- Size of remote file:
- 1.26 GB
- SHA256:
- ea878546e2ef83289d9434e2134f42f0e559ffc708eda7b40bae098f817f4808
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