Instructions to use OATML-Markslab/Tranception_Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OATML-Markslab/Tranception_Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="OATML-Markslab/Tranception_Small")# Load model directly from transformers import AutoModelWithLMHead model = AutoModelWithLMHead.from_pretrained("OATML-Markslab/Tranception_Small", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50b35aafe3fc076d51e9636d593e32b7688d3de3def94c876a7524937f77f9cc
- Size of remote file:
- 353 MB
- SHA256:
- a87668e727b13f45f41fe2f9c47768b7353664629ca126fe163d33eff32fd4a9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.