Instructions to use Sjdan/sw_hp4_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/sw_hp4_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/sw_hp4_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/sw_hp4_2") model = AutoModelForCTC.from_pretrained("Sjdan/sw_hp4_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/sw_hp4_2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/sw_hp4_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/sw_hp4_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/sw_hp4_2/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 7c72c41bac7f2d2f8e86208eb5c4be13e30b20774abbf08f955f924255cb2365
- Size of remote file:
- 1.26 GB
- SHA256:
- 39b63ef105989f215919d1387f0cbfd1c3873dcbef1c1d7b7f5aa4b34570dcb8
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