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 training_args.bin from Sjdan/m14: direct link, hf CLI and curl.
- Browser
- Download file 3 kB
-
https://huggingface.co/Sjdan/m14/resolve/main/training_args.bin
- Command line
-
hf download hf://Sjdan/m14/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sjdan/m14/resolve/main/training_args.bin
3 kB
- Xet hash:
- 51d3a8e77cfd101ec7f68d3e2a3911485a75fd877bbd065cfed9782d368ef9be
- Size of remote file:
- 3 kB
- SHA256:
- 4528a1695c7929dd98bf66747006eaf00107238fad77ff92eac2ba5f426925b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.