Instructions to use jadasdn/repo_name with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jadasdn/repo_name with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jadasdn/repo_name")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("jadasdn/repo_name") model = AutoModelForSpeechSeq2Seq.from_pretrained("jadasdn/repo_name", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a0204e76ffd7d5c149240d1f76c8c4d8737f6f581d36f35d041b2ee40c1771e
- Size of remote file:
- 4.73 kB
- SHA256:
- f3bb25c1283fb2e1f1e7d8c1fb088b2df896d6682bcf145ed3dd56a711863cd2
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