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:
- 767dc0ae72e0bbafc8fd3f8b54bf4dfcd61f71dd05d9f330cac7200967d2739d
- Size of remote file:
- 3.92 MB
- SHA256:
- f7d8560cbbfa14d950cd5e9cad343a2274ea01a14260f748b02ea68f525968c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.