Instructions to use hf-tiny-model-private/tiny-random-LongT5Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LongT5Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-LongT5Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongT5Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-LongT5Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 446ddd73da47c2641d1db2e18b94e03de1ba2b3071841c9f3e24971a08be8d80
- Size of remote file:
- 4.47 MB
- SHA256:
- 88fecd1d3c99d23cc9cc736a291817269e71dd4741f7c3819df2656db132f042
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.