Instructions to use Zamachi/bart-for-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zamachi/bart-for-summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Zamachi/bart-for-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Zamachi/bart-for-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Zamachi/bart-for-summarization: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/Zamachi/bart-for-summarization/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Zamachi/bart-for-summarization/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Zamachi/bart-for-summarization/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- db7681e2b92f76cbcba43d531f1d4ea29dc2ec2681da878c22e3644046ac892e
- Size of remote file:
- 1.63 GB
- SHA256:
- ef55ed83990a172c913b1419845894a847ffbddaa1099611ca25dfdd17e11db6
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