Instructions to use krm/BARTkrame-abstract-mT5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krm/BARTkrame-abstract-mT5 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="krm/BARTkrame-abstract-mT5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("krm/BARTkrame-abstract-mT5") model = AutoModelForSeq2SeqLM.from_pretrained("krm/BARTkrame-abstract-mT5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from krm/BARTkrame-abstract-mT5: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/krm/BARTkrame-abstract-mT5/resolve/main/training_args.bin
- Command line
-
hf download hf://krm/BARTkrame-abstract-mT5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/krm/BARTkrame-abstract-mT5/resolve/main/training_args.bin
3.5 kB
- Xet hash:
- e0b6474c59e96965e680f769bc5b47a96d2ff13a392c3d50aaacd4e397a6f69c
- Size of remote file:
- 3.5 kB
- SHA256:
- d6a91676a4a111ea6d50eddf441da15e3109766db298797f75a82667f5d9e112
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