Instructions to use hf-tiny-model-private/tiny-random-LiltForQuestionAnswering 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-LiltForQuestionAnswering 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-LiltForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LiltForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-LiltForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32cd370d46ffdca48b7322ef32de2ad4d5c7bd585875b88161904b1781f2195a
- Size of remote file:
- 294 kB
- SHA256:
- 00364ae78f02485b4025a2631a1f64a5473fc64d57bdf6018b3ad9b453f09250
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