Instructions to use trl-internal-testing/tiny-BartModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-BartModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="trl-internal-testing/tiny-BartModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-BartModel") model = AutoModel.from_pretrained("trl-internal-testing/tiny-BartModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f0161d0b98176d17928255e20cc02b0c39fd51420f0b3e1fd3648bebd03664f
- Size of remote file:
- 12.5 MB
- SHA256:
- 621f203e0f27672fae668ac61137c800e9dbf5f2c820235ae2d426f18fb4f474
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.