Instructions to use Sjdan/f05 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/f05 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/f05")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/f05") model = AutoModelForCTC.from_pretrained("Sjdan/f05", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/f05: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/f05/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/f05@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/f05/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.26 GB
- Xet hash:
- 58b07fe8c730e60e6a2afe4c65ac756164b9a5dd26402e3a1fbec4993047c5e9
- Size of remote file:
- 1.26 GB
- SHA256:
- 8215e8d85a34fd8364ffc4815c281104f2ab6a7f91bd103070472b2a5a2e5853
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