Instructions to use SaladSlayer00/Prov_SML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaladSlayer00/Prov_SML with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="SaladSlayer00/Prov_SML")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("SaladSlayer00/Prov_SML") model = AutoModelForSpeechSeq2Seq.from_pretrained("SaladSlayer00/Prov_SML", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30793a1c9f45834384739379f5bcdb6d40729331c3e82ebda6e5c295320382d5
- Size of remote file:
- 4.79 kB
- SHA256:
- ba24686227c0ff8875b2e867ac0d3152c538d329fe3849bb4e20c2c2dd0ad1a1
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