Instructions to use hf-internal-testing/tiny-random-BlipForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BlipForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="hf-internal-testing/tiny-random-BlipForQuestionAnswering")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-BlipForQuestionAnswering") model = AutoModelForVisualQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-BlipForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9986f3babb9f7b27b7a091d1d38e4e2f04bbbaac55c8fa3ba4b176f7981d367
- Size of remote file:
- 852 kB
- SHA256:
- 432b59ef08dfb47a00c74ed2889bf09e4eca13d671309ec17e2360d0f5086980
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.