Instructions to use hf-internal-testing/tiny-random-PLBartModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PLBartModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-PLBartModel")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-PLBartModel") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-PLBartModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b01807189764439b8daef4e7a6bba3cf076ad128fda86c9e9912b239d47a941
- Size of remote file:
- 3.27 MB
- SHA256:
- aed2487a8fcc2d633f1d277d0c2a073e832ab2b8c037f178e76fbfda04f846e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.