Instructions to use hf-internal-testing/tiny-random-bigbird_pegasus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-bigbird_pegasus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-bigbird_pegasus")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bigbird_pegasus") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bigbird_pegasus") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 681f445b3493ab96f9ff1373dfeeed1158b4dca6b4100a363c6986451d5665cc
- Size of remote file:
- 336 kB
- SHA256:
- 2a5a5fd24fe52b58464f2db487955a30fd5fb979c774025fee1e6db8b8faef53
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.