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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3de4858ef36961bcb68d9c788a26f198978d962407e7bd3455a425e6477c35c7
- Size of remote file:
- 802 MB
- SHA256:
- 1a3f853c0a49d13cb5b78b25bc671df452371fcb8fd16013a8ae7062f75e5e28
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