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:
- 058cc8c06a990e478d47f9382ce65bdbae9ac4a9d801930b84893258e719066a
- Size of remote file:
- 3.92 MB
- SHA256:
- fa483a251d27d0861ff1a56a59691d3d2e78aeb6a13eb4fe64d9d3670f753a3d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.