Instructions to use learn3r/bart_memsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use learn3r/bart_memsum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("learn3r/bart_memsum") model = AutoModelForSeq2SeqLM.from_pretrained("learn3r/bart_memsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from learn3r/bart_memsum: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/learn3r/bart_memsum/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://learn3r/bart_memsum/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/learn3r/bart_memsum/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- 92a9a96a9f6256a21fdaa8d04b3a45a41b5400ade57ff1533fa15bf7e823bb05
- Size of remote file:
- 558 MB
- SHA256:
- 07afd4d7a5fd0fffc667358dcb2f1d7690a1ca3355e59c13edab92ce3d5fba00
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