Instructions to use kavyamanohar/Malasar_Luke_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kavyamanohar/Malasar_Luke_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kavyamanohar/Malasar_Luke_small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kavyamanohar/Malasar_Luke_small") model = AutoModelForSpeechSeq2Seq.from_pretrained("kavyamanohar/Malasar_Luke_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c04114d37242acfd3cc0d6dba9f998592110624bd254c083a74e7f9827ce8a8
- Size of remote file:
- 967 MB
- SHA256:
- daf16b370ea649e164f4afd0df3615b29fc0175d560ff4e0acd63e9acc69c9ab
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