Instructions to use hf-internal-testing/tiny-random-LEDForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LEDForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-LEDForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LEDForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-LEDForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7eb9ce0a07d321335828cf0d95a270e4db88ac05e09c9194f363f5dcf0deb4de
- Size of remote file:
- 1.25 MB
- SHA256:
- ce74ba779906db5c891d37021dfa57bac7e3710f26c90345c52a710095791e06
路
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