Instructions to use onnx-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onnx-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="onnx-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("onnx-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("onnx-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 250487f4f86c674d7f40ad96f23fd9769f63d624a776a640f189602995224ef6
- Size of remote file:
- 16.8 MB
- SHA256:
- d47c89de43f5d0763ec74fd4fc6a6bef0d53262cd4f0e647557ea246a8dafd45
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