Instructions to use hf-internal-testing/tiny-random-T5ForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-T5ForConditionalGeneration with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-T5ForConditionalGeneration") model = AutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-T5ForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3aeb72908c728e902d8ed3cb920065c060db6d238f5cd688612226b72176eec9
- Size of remote file:
- 4.49 MB
- SHA256:
- 3ef2bdc2ed138313950c7edaaeaba2566d395280d58556f66fd78bd8311a05e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.