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:
- 7993dc7243a8dab5ba1e98ee99fe5fe526d6bdd4b391f1d9d165ff57b69a02ba
- Size of remote file:
- 265 MB
- SHA256:
- fd9ba9fabe000fd0ae8c43340175d68720cbf505cefcb213daff92a0d89d9207
路
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