Instructions to use hf-internal-testing/tiny-random-LongT5Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LongT5Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LongT5Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LongT5Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LongT5Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e735b344a5ac88b2bad75ed1e3ab38dbab1011b0a1ceb728739d0d9ae86a3b18
- Size of remote file:
- 4.49 MB
- SHA256:
- b2fcc5fc54ac623548f30b7ec96b0c07b2f6153bd904d2b769ec9e0cd084fca3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.