Instructions to use hf-internal-testing/tiny-random-NystromformerForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-NystromformerForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-NystromformerForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-NystromformerForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-NystromformerForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff60a76de71be71b6794125d3f68a795983c8b647e032cd6af1fafb07fcdbd80
- Size of remote file:
- 761 kB
- SHA256:
- 39ee5d0cd20ab9616138d90a6d9c811ed41aba04a95d515bee6d4d5cd42ef0a6
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.