Instructions to use psxjp5/bart-large-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use psxjp5/bart-large-xsum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("psxjp5/bart-large-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("psxjp5/bart-large-xsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from psxjp5/bart-large-xsum: direct link, hf CLI and curl.
- Browser
- Download file 2.04 GB
-
https://huggingface.co/psxjp5/bart-large-xsum/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://psxjp5/bart-large-xsum/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/psxjp5/bart-large-xsum/resolve/main/pytorch_model.bin
2.04 GB
- Xet hash:
- 37c3f150b2e5dcb198f48e4e9bfd169980b0ef5a6ef6a63b21267fc66d6f014c
- Size of remote file:
- 2.04 GB
- SHA256:
- 2df5dcfc8df9951230bef235f3468db60714f9dc35060ea240f45c0f5558b10d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.