Instructions to use hf-internal-testing/tiny-random-MegaForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MegaForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-MegaForQuestionAnswering")# Load model directly from transformers import AutoModelForQuestionAnswering model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-MegaForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0952aa102d4fa6b7d9ed946c59dd174ddbf41d6df8706f1e60676b003bec8286
- Size of remote file:
- 406 kB
- SHA256:
- f396945f0290a75b180c4b031e26796f17dc808ec52c4842795460ba8022a715
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.