Instructions to use devagonal/flan-t5-base-squad-qag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devagonal/flan-t5-base-squad-qag with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("devagonal/flan-t5-base-squad-qag") model = AutoModelForSeq2SeqLM.from_pretrained("devagonal/flan-t5-base-squad-qag", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a3c3d2b1c519d3775b113260870196b63cbc14ea2b90d148703fa98d3ead884
- Size of remote file:
- 990 MB
- SHA256:
- 658fe126de3aff1db7948e1191610f24e9e043bd78e658041ffd0444f5117bac
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.