Instructions to use jasonsurya0/BART_ONE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jasonsurya0/BART_ONE with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jasonsurya0/BART_ONE") model = AutoModelForSeq2SeqLM.from_pretrained("jasonsurya0/BART_ONE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jasonsurya0/BART_ONE: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/jasonsurya0/BART_ONE/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jasonsurya0/BART_ONE/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jasonsurya0/BART_ONE/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- b4f3f0c1354b45ee54880d47557ea5edb9286f3937a69b6ffdd6fad0f2b97011
- Size of remote file:
- 1.63 GB
- SHA256:
- 3cffa627bc424d8099226d13c65dee2a326f02f07541332b00f9c5b9062b2710
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.