Instructions to use ARTeLab/mbart-summarization-ilpost with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARTeLab/mbart-summarization-ilpost 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="ARTeLab/mbart-summarization-ilpost")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ARTeLab/mbart-summarization-ilpost") model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/mbart-summarization-ilpost", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ARTeLab/mbart-summarization-ilpost: direct link, hf CLI and curl.
- Browser
- Download file 2.44 GB
-
https://huggingface.co/ARTeLab/mbart-summarization-ilpost/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ARTeLab/mbart-summarization-ilpost/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ARTeLab/mbart-summarization-ilpost/resolve/main/pytorch_model.bin
2.44 GB
- Xet hash:
- 2c010cdeb93dff9195cee948610d2678cb9942f663355548cd8da5035a2853b3
- Size of remote file:
- 2.44 GB
- SHA256:
- 5012676683febe9eaa832c566d4528ba148f0b31239ba8ad72b6b3c9b43f595f
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