Instructions to use Sjdan/sd_ft_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/sd_ft_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/sd_ft_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/sd_ft_1") model = AutoModelForCTC.from_pretrained("Sjdan/sd_ft_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Sjdan/sd_ft_1: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/Sjdan/sd_ft_1/resolve/main/training_args.bin
- Command line
-
hf download hf://Sjdan/sd_ft_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sjdan/sd_ft_1/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- 0a6adb50bf2cbdff9f0ded8ec81adf65d3e856dde10d59d1647146ee9d1ebd78
- Size of remote file:
- 3.45 kB
- SHA256:
- a41739d22bfe13d133db825e4c2db2911e90c8dc19d27b7ade1a9d74b160382d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.