Instructions to use Vkt/model-facebookptbrlarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vkt/model-facebookptbrlarge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Vkt/model-facebookptbrlarge")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Vkt/model-facebookptbrlarge") model = AutoModelForCTC.from_pretrained("Vkt/model-facebookptbrlarge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Vkt/model-facebookptbrlarge: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Vkt/model-facebookptbrlarge/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Vkt/model-facebookptbrlarge/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Vkt/model-facebookptbrlarge/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 7f87c4a937c67d4dcdb4cd0173a99a353816623e59ca651df7d1133390a6b013
- Size of remote file:
- 1.26 GB
- SHA256:
- 0165d7b0db9c21e575078f9d727cd65d95b60bc32c84fadd0a2bfaf8219fe7b5
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