Instructions to use NbAiLabArchive/test_OSCAR_flax with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_OSCAR_flax with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_OSCAR_flax")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_OSCAR_flax") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_OSCAR_flax") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d79020aa0cf66ac18bf075e01947d6387d226aed310102d01960a7454fa0107e
- Size of remote file:
- 499 MB
- SHA256:
- 87d1908af15f8d9d4b42b84dc54a3da3a0e9cacd81ab71a9152bcd12bfbd3791
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.