Instructions to use hf-internal-testing/tiny-random-BartModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BartModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-BartModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BartModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-BartModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9c167a3db2041c6008831595bc98b759bbc4f2e7dea98592bcce5c70b6d135e
- Size of remote file:
- 137 kB
- SHA256:
- f56563cce147eee4d953fc52098ded6331b3235dbfd13064b077b12b8b56dfe1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.