Instructions to use Sjdan/switch_low_960h_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/switch_low_960h_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/switch_low_960h_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/switch_low_960h_2") model = AutoModelForCTC.from_pretrained("Sjdan/switch_low_960h_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/switch_low_960h_2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/switch_low_960h_2/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/switch_low_960h_2@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/switch_low_960h_2/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.26 GB
- Xet hash:
- 7fc5d39e14d22f1f3bc128f8bd2f65fa19dbd6cb54cd193d6a55bf87a4866195
- Size of remote file:
- 1.26 GB
- SHA256:
- d8dfc53eb051f2ea0b25d64a25921b974888bfcb4d58b9f51f0448ec2ea54af7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.