Instructions to use hf-internal-testing/tiny-random-Speech2TextModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Speech2TextModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-Speech2TextModel")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Speech2TextModel") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-Speech2TextModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b2c361e61db1d0894c4d4a9adf7156fa7fe4a136c49f7f841be0da1c36376bd
- Size of remote file:
- 809 kB
- SHA256:
- 3c8f832042f2453945c18d5c37164ce10854f04cadc06ec9b0299babde8aa601
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.