Instructions to use hf-internal-testing/tiny-random-TvpModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-TvpModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-TvpModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-TvpModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-TvpModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ee0f4bf616d69a3001a333e2cf7090d8bd9d68b81000134230bf1b1f86c081c
- Size of remote file:
- 3.92 MB
- SHA256:
- ec241d3168b85a7f0e4598a02ee2a04531b7c80c791bce32fe57805336e70d28
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.