Instructions to use vadis/xscitldr_de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vadis/xscitldr_de with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vadis/xscitldr_de", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from vadis/xscitldr_de: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/vadis/xscitldr_de/resolve/main/tokenizer.json
- Command line
-
hf download hf://vadis/xscitldr_de/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/vadis/xscitldr_de/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 1d81eb37c70465b9bcda19baa6078f10e5a25aea542e2d3b9dab771afdf37e50
- Size of remote file:
- 17.1 MB
- SHA256:
- 0df8a0319e6f0464bfac55a5283a8f8ce4a1fb06203fdd2c76ae59c271198e0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.