Instructions to use jayanta/bart-large-cnn_summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayanta/bart-large-cnn_summary with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jayanta/bart-large-cnn_summary") model = AutoModelForSeq2SeqLM.from_pretrained("jayanta/bart-large-cnn_summary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jayanta/bart-large-cnn_summary: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/jayanta/bart-large-cnn_summary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jayanta/bart-large-cnn_summary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jayanta/bart-large-cnn_summary/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- f2d39a9165372427da1da95ffb4100e28320cd53f55052323c84c65431587e1c
- Size of remote file:
- 1.63 GB
- SHA256:
- f8748bd992202eab069f4694cb59badf956f7a479fb2afc1f225535509bc90a6
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