Instructions to use Sjdan/m14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/m14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/m14")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/m14") model = AutoModelForCTC.from_pretrained("Sjdan/m14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/m14: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/m14/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/m14/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/m14/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- ee35e4f1d162165c065704427d7901bcb0eea78d18a3593d6a9a21842fc13f8b
- Size of remote file:
- 1.26 GB
- SHA256:
- 2f1bdffab9fba3ded496a6c7ad753712529ecb6ac4affe6a30928d5223c635b3
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