Instructions to use kejian/final-mle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kejian/final-mle with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kejian/final-mle") model = AutoModel.from_pretrained("kejian/final-mle", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07e771fddffae9a3c824dbcd8006447f8493712612e2a5ffb413bc862775e7b4
- Size of remote file:
- 457 MB
- SHA256:
- 3b64690a17859331e17b4c1156764a6d6a472253c8c347e811d1c41677dc8fab
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.