Instructions to use machinelearningzuu/gem-qna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use machinelearningzuu/gem-qna with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="machinelearningzuu/gem-qna")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("machinelearningzuu/gem-qna") model = AutoModelForQuestionAnswering.from_pretrained("machinelearningzuu/gem-qna", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1127ca4bc862a14d75c8a0cfa20e3872a7f3873ffbd0a2d0979cd9a19dba75ff
- Size of remote file:
- 265 MB
- SHA256:
- 57cf73945194d8963da2d9f1b4577165b7fbb61116daeccc1104133dc7b7b979
路
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