Instructions to use hf-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-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="hf-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8652ed4450a71358e609d6a76f9a4c1279bfb0472ea868f5e7df61f265ccb6cb
- Size of remote file:
- 32.1 kB
- SHA256:
- 788c2e4a0f12486a84f88d548ab5a03df8d128c01b73584df458a52663180c86
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