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:
- 5de9dd76def0fbdbffa6d403980ca85b38dfb6f73db0673aee159716b9907981
- Size of remote file:
- 12.9 MB
- SHA256:
- 560df21c5462bc25e5efd51befb727c7eee002ac5fec7f0467f6e70d2f447e2b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.