Instructions to use MikhailKuz/tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MikhailKuz/tmp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="MikhailKuz/tmp")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("MikhailKuz/tmp") model = AutoModelForDocumentQuestionAnswering.from_pretrained("MikhailKuz/tmp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3bc9887c9095fbd1495fc9ae3a8842912d2ccd98cb9ea4ca076ca65d54755ae3
- Size of remote file:
- 5.18 kB
- SHA256:
- 611af3db1929a46fc7dc8725c887001587b55ca541f2a5bc96f56c6b18486e54
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