Instructions to use Baicai003/tiny-t5-one with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Baicai003/tiny-t5-one with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Baicai003/tiny-t5-one") model = AutoModelForSeq2SeqLM.from_pretrained("Baicai003/tiny-t5-one", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d58d6c315cfaf8cce8e36e631a747ca94d77922d670b7cb75ef978f65ace8d6
- Size of remote file:
- 139 kB
- SHA256:
- 4cd32553d3d41659ca462eebada5a810c49a16486ab98cd1bfd6c343ec0a8cd0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.