Instructions to use jasonsurya0/BART_TEN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jasonsurya0/BART_TEN with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jasonsurya0/BART_TEN") model = AutoModelForSeq2SeqLM.from_pretrained("jasonsurya0/BART_TEN", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jasonsurya0/BART_TEN: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/jasonsurya0/BART_TEN/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jasonsurya0/BART_TEN/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jasonsurya0/BART_TEN/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- edd8bf9445289d52a61f8da2beb957d6aa4e74a2b7e491fe9fcbde744ee02f88
- Size of remote file:
- 1.63 GB
- SHA256:
- 3f69c7cba471083f02f4ce970c03437fa69eb03a375736ce5d2acecedc69f2e6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.