Instructions to use obss/bsm-pegasus-xsum-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use obss/bsm-pegasus-xsum-multi with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("obss/bsm-pegasus-xsum-multi") model = AutoModelForSeq2SeqLM.from_pretrained("obss/bsm-pegasus-xsum-multi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from obss/bsm-pegasus-xsum-multi: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/obss/bsm-pegasus-xsum-multi/resolve/main/training_args.bin
- Command line
-
hf download hf://obss/bsm-pegasus-xsum-multi/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/obss/bsm-pegasus-xsum-multi/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- f426ea2cb64eb6dd0800884488a09cfe78923ff12479faed9dd5ad005e7d2558
- Size of remote file:
- 3.31 kB
- SHA256:
- 69d9fd3ad18563c1684c7b33cda0775c0390d2503688d8228ae9022dc5e43c7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.