Instructions to use hf-tiny-model-private/tiny-random-LEDForQuestionAnswering 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-LEDForQuestionAnswering 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-LEDForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LEDForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-LEDForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9059b65ddc3027ec5a1aeea365ab21286b2084a93bbbb6dace8d59ff49ff7cdb
- Size of remote file:
- 1.25 MB
- SHA256:
- 4f373670114842cf88b2516d40646ab80fe215a746bb7cebc0f2b18a490408f8
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