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:
- bf48c48fd301a0e5a74d775b41723825d1bc20554bb31c4c14b9ee8b88adca36
- Size of remote file:
- 3.92 MB
- SHA256:
- a5d40b173b2c6e5b839fdc1d0688a3f335e4ac6006da28673c0828cb16d45be1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.