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:
- 308841f4127ea871c52c4a18795a100d5eddfd02c61100fb9cf3e83a8dc5cd52
- Size of remote file:
- 4.22 kB
- SHA256:
- a4e658f65fce35759ac653d51b7bae3259eca76992319c62e92df3c93bf340e0
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