Instructions to use hf-tiny-model-private/tiny-random-MegaForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MegaForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-MegaForQuestionAnswering")# Load model directly from transformers import AutoModelForQuestionAnswering model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-MegaForQuestionAnswering", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a17c10244648100835a306b6ac8ec330fe23cb98a230f47d641ece57bc222b7
- Size of remote file:
- 406 kB
- SHA256:
- f2382cb573c81225f4acdeb9b178812769b8fe0eeeac812e6e8d1a622ca85ecb
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.