Instructions to use Hansollll/summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hansollll/summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Hansollll/summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Hansollll/summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Hansollll/summarization: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/Hansollll/summarization/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Hansollll/summarization/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Hansollll/summarization/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- c2dec23f3131ca6e5c648a6e9948a7fe03f6c8f2285ec97834406c3a2856dbc1
- Size of remote file:
- 496 MB
- SHA256:
- b592b552d6b81014371c21292029c782b033ab99c1f2a0af7f190c53dc05760c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.