Instructions to use GanjinZero/biobart-v2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GanjinZero/biobart-v2-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("GanjinZero/biobart-v2-base") model = AutoModelForSeq2SeqLM.from_pretrained("GanjinZero/biobart-v2-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from GanjinZero/biobart-v2-base: direct link, hf CLI and curl.
- Browser
- Download file 666 MB
-
https://huggingface.co/GanjinZero/biobart-v2-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://GanjinZero/biobart-v2-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GanjinZero/biobart-v2-base/resolve/main/pytorch_model.bin
666 MB
- Xet hash:
- 414f9838e1623010d94f77aaf1c1baf288d6e89cec88e5fd705e3e5143f4c0d0
- Size of remote file:
- 666 MB
- SHA256:
- 64e8b2f3605573dbc28d9383ccbda96d30ff7805cd30780503df73cfe9a83d3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.