Instructions to use luciajw/rand85 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luciajw/rand85 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("luciajw/rand85", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec0d43831c086fe596bf3e93f158474de516ef695ada88d5ad4868d5324b5c7d
- Size of remote file:
- 5.91 kB
- SHA256:
- 84003f873a9810088157ba8842d847fdf8e49f4d7d2d97f89ae02d60ee85efd7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.