Instructions to use AI4PD/REXzyme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4PD/REXzyme with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AI4PD/REXzyme") model = AutoModelForSeq2SeqLM.from_pretrained("AI4PD/REXzyme", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_dataset.json from AI4PD/REXzyme: direct link, hf CLI and curl.
- Browser
- Download file 13.3 GB
-
https://huggingface.co/AI4PD/REXzyme/resolve/main/training_dataset.json
- Command line
-
hf download hf://AI4PD/REXzyme/training_dataset.json
-
curl -L -o training_dataset.json https://huggingface.co/AI4PD/REXzyme/resolve/main/training_dataset.json
13.3 GB
- Xet hash:
- 7eafd08161b30c9b639b3f1354f70583d66e2267e70ef28e86de742c22b53e9d
- Size of remote file:
- 13.3 GB
- SHA256:
- 01355ca1636664ec91dd68df7315ea602dc6a1cae24968bd12160ab1245cd486
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.