Instructions to use jrc-ai/PreDA-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jrc-ai/PreDA-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jrc-ai/PreDA-base") model = AutoModelForSeq2SeqLM.from_pretrained("jrc-ai/PreDA-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jrc-ai/PreDA-base: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/jrc-ai/PreDA-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jrc-ai/PreDA-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jrc-ai/PreDA-base/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- b055172df8b55e084547fb27b13687cbf9f1c735a8e085cfb1c9176d901796df
- Size of remote file:
- 892 MB
- SHA256:
- a74108262b15383e3ed5c39f94414253ca7278424bc14de2cada15f1bb654ffa
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