Instructions to use onionLad/omission-bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onionLad/omission-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/omission-bart")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("onionLad/omission-bart") model = AutoModelForSeq2SeqLM.from_pretrained("onionLad/omission-bart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from onionLad/omission-bart: direct link, hf CLI and curl.
- Browser
- Download file 4.78 kB
-
https://huggingface.co/onionLad/omission-bart/resolve/main/training_args.bin
- Command line
-
hf download hf://onionLad/omission-bart/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/onionLad/omission-bart/resolve/main/training_args.bin
4.78 kB
- Xet hash:
- c8c9fe2de1dfa17bb6d63de092cfa4ca67274154e5148c0c08ea595fb801f47b
- Size of remote file:
- 4.78 kB
- SHA256:
- 7cac31ebbc0121ccab22d88cd5e8d73c71c1de719037aa8ef8e5a69ec615af8e
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