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:
- f3fa99a68e92c3aa01cd1eeb36083a29d847e873cdae0e2e54dcd335b689454a
- Size of remote file:
- 852 kB
- SHA256:
- ba27a7cc759aa6fe06d83f31b91819c4e60a5e9516843882786031223ca4f96c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.