Instructions to use Patcas/plbart-docs-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/plbart-docs-v3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Patcas/plbart-docs-v3") model = AutoModelForSeq2SeqLM.from_pretrained("Patcas/plbart-docs-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Patcas/plbart-docs-v3: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/Patcas/plbart-docs-v3/resolve/main/training_args.bin
- Command line
-
hf download hf://Patcas/plbart-docs-v3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Patcas/plbart-docs-v3/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 776130e86806db5bfd57b3653102801ad72e55128da7cf9e815b6c25a560e4af
- Size of remote file:
- 4.73 kB
- SHA256:
- e09d781e0a23dda9b6f62f6a84ce530378f66210e07c8deaaeff7c0df31e7f00
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.