Instructions to use davidcechak/DNADebertaK8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidcechak/DNADebertaK8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="davidcechak/DNADebertaK8")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("davidcechak/DNADebertaK8") model = AutoModelForMaskedLM.from_pretrained("davidcechak/DNADebertaK8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from davidcechak/DNADebertaK8: direct link, hf CLI and curl.
- Browser
- Download file 388 MB
-
https://huggingface.co/davidcechak/DNADebertaK8/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://davidcechak/DNADebertaK8/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/davidcechak/DNADebertaK8/resolve/main/pytorch_model.bin
388 MB
- Xet hash:
- 24140de008a15b3abe3af8acdf78db3eb542cb12e9ea51ac3b3de843cf5a9b75
- Size of remote file:
- 388 MB
- SHA256:
- 486a46c7c37e39e6cb3d99bc0584f44f050a6bbd8b46483e703e2d3e5ce50792
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.