Instructions to use mcurmei/flat_N_max with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcurmei/flat_N_max with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mcurmei/flat_N_max")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mcurmei/flat_N_max") model = AutoModelForQuestionAnswering.from_pretrained("mcurmei/flat_N_max") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7de80946eda9dc24ade963129141fc4f7e05357a51688cf583cbec54fc8de7ec
- Size of remote file:
- 3.06 kB
- SHA256:
- 565b91a6b9df76d14ce0b6a685f04e4923fa383dbd81f964b9dd7076c2b3124c
路
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