Instructions to use amaibrah/long_doc_summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amaibrah/long_doc_summarizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("amaibrah/long_doc_summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("amaibrah/long_doc_summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from amaibrah/long_doc_summarizer: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/amaibrah/long_doc_summarizer/resolve/main/training_args.bin
- Command line
-
hf download hf://amaibrah/long_doc_summarizer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/amaibrah/long_doc_summarizer/resolve/main/training_args.bin
3.5 kB
- Xet hash:
- 3ba8e979a40dd10135f9adaee93c09b12601232e7b542d7288c990ec8ace6439
- Size of remote file:
- 3.5 kB
- SHA256:
- c53c1308ab14150ead4a55b8a5f75e09bf885c42dde330bae55bb51223bd8c35
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.