Instructions to use emilstabil/DanSumT5-largeV_84227 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emilstabil/DanSumT5-largeV_84227 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("emilstabil/DanSumT5-largeV_84227") model = AutoModelForSeq2SeqLM.from_pretrained("emilstabil/DanSumT5-largeV_84227", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from emilstabil/DanSumT5-largeV_84227: direct link, hf CLI and curl.
- Browser
- Download file 4.92 GB
-
https://huggingface.co/emilstabil/DanSumT5-largeV_84227/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://emilstabil/DanSumT5-largeV_84227/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/emilstabil/DanSumT5-largeV_84227/resolve/main/pytorch_model.bin
4.92 GB
- Xet hash:
- 6ed7d870c2c76c45a455089964ee0730cf3142a14feecfc9bfc504f8b0e4fb26
- Size of remote file:
- 4.92 GB
- SHA256:
- 918ad1c514665e7fa8c96f6bbd5a028c51ed99c687ef2c477b9bc190e43a3a19
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.