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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-TvpModel") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-TvpModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90ed9eb19534734b8d0a9991c52782b244ff56be1e7e10fc82b654115861d6dd
- Size of remote file:
- 3.92 MB
- SHA256:
- 8b4f539b2386e20c92fa30dec9af95027df889eb33265b7d9f05598f172154ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.