Instructions to use fusing/gpt2_optimus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fusing/gpt2_optimus with Transformers:
# Load model directly from transformers import AutoTokenizer, GPT2OptimusForLatentConnector tokenizer = AutoTokenizer.from_pretrained("fusing/gpt2_optimus") model = GPT2OptimusForLatentConnector.from_pretrained("fusing/gpt2_optimus") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76f8ea03dabe081042fa5105e4e9784efac73f608c542e742e83e7037fd58a75
- Size of remote file:
- 541 MB
- SHA256:
- 78cbfed892cfa069c9d5843c4896bcece79cc888a3ee710954948a5160b3eda9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.