Instructions to use AdoCleanCode/LAVCO-codec-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdoCleanCode/LAVCO-codec-v2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AdoCleanCode/LAVCO-codec-v2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbd617679abdac7513a1d01ba0b6aa889d858b44217043f47d208dc10438034a
- Size of remote file:
- 11.7 MB
- SHA256:
- b1b128d721f35b2bf30a508f66e858e6f22a398c44569618909ef11e6cb470ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.