Instructions to use hf-internal-testing/tiny-random-PegasusModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PegasusModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-PegasusModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-PegasusModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-PegasusModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3511192f8ddb5fa743f36c58e16a80b22f706279c2880b9d1d2ce60c4da6ccb1
- Size of remote file:
- 6.23 MB
- SHA256:
- fee7599a240b4bb23aa38cb786b8b85114eb893286ff4fc7a51f814239110205
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.