Instructions to use whu9/multi_doc_sum_slide_token with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whu9/multi_doc_sum_slide_token with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, NetSum tokenizer = AutoTokenizer.from_pretrained("whu9/multi_doc_sum_slide_token") model = NetSum.from_pretrained("whu9/multi_doc_sum_slide_token", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from whu9/multi_doc_sum_slide_token: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/whu9/multi_doc_sum_slide_token/resolve/main/training_args.bin
- Command line
-
hf download hf://whu9/multi_doc_sum_slide_token/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/whu9/multi_doc_sum_slide_token/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- 8dcd8bc407157f09004592e63793a05199ba5b74de0ffb8749b2bdcfbac2d2b4
- Size of remote file:
- 3.18 kB
- SHA256:
- 574bb7f2d8953fd5726861cacc32edc40ed2547683188f04559eccb84282213a
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