Instructions to use narySt/LLM_project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use narySt/LLM_project with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("narySt/LLM_project", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 714f4c82c3e8b7e0620e5d50dd43a74a7487da9f93351f0eabedd2b13fce06c2
- Size of remote file:
- 1.26 GB
- SHA256:
- b76e295424cb831c1e09cef03e97e28059d5d219339cd5521434ab3b1ade92a5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.