Instructions to use UdS-LSV/smole-bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UdS-LSV/smole-bart with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("UdS-LSV/smole-bart") model = AutoModelForSeq2SeqLM.from_pretrained("UdS-LSV/smole-bart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d8177df0660f0627cbe98dac56307291943175eb37c8e46f87e6910492eff99
- Size of remote file:
- 21.8 MB
- SHA256:
- ed999349e2762e2006078d9179f01ac5b0ecce3cf1ad47e97dde2d7e93c369f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.