Instructions to use hf-internal-testing/tiny-random-ClapModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ClapModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-ClapModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-ClapModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ClapModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64214df76a79fe0eeaf2b91f9cef5140dbf13cc45661592f9dfd9f280d891044
- Size of remote file:
- 12.9 MB
- SHA256:
- c6157a26bf39d27284bcd686d7c92e70a97df78faae73e5f31cd42d4b24cbab9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.