Instructions to use onionLad/generalize-bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onionLad/generalize-bart with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="onionLad/generalize-bart")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("onionLad/generalize-bart") model = AutoModelForSeq2SeqLM.from_pretrained("onionLad/generalize-bart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from onionLad/generalize-bart: direct link, hf CLI and curl.
- Browser
- Download file 4.78 kB
-
https://huggingface.co/onionLad/generalize-bart/resolve/main/training_args.bin
- Command line
-
hf download hf://onionLad/generalize-bart/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/onionLad/generalize-bart/resolve/main/training_args.bin
4.78 kB
- Xet hash:
- 7486fa1e1582e892a3b17dd88f301f377de1134ed49449cf10c394d0e4c8dae8
- Size of remote file:
- 4.78 kB
- SHA256:
- 359cafd59a26cc945048f1bbe9609143ffccc6900c4eaedcd8b3c679cd885e85
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