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 pytorch_model.bin from Sjdan/sd_ft_1: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/sd_ft_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/sd_ft_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/sd_ft_1/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- bcb7115da61fe584fb867698bab1d6b699cad9148d2faa1a3f9de1a59e701796
- Size of remote file:
- 1.26 GB
- SHA256:
- 8863068d762e172b35629c9dde1fbae832ae5343fd8d04b4899e46e330d5db22
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.