Instructions to use onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration 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-GraniteSpeechForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration") model = AutoModelForMultimodalLM.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7df6ca648a01e0094b0ad6ac7def8292a6de906d0d7e22e93bdf7ccd65b7eed
- Size of remote file:
- 41.2 kB
- SHA256:
- 9a82b08bef7d8c5b889d5a2c4d1bdd8e09c753210c12395b5c12985f5e1ab73e
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