Instructions to use wesley7137/BlipFinetune-ScienceQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesley7137/BlipFinetune-ScienceQA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="wesley7137/BlipFinetune-ScienceQA")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("wesley7137/BlipFinetune-ScienceQA") model = AutoModelForVisualQuestionAnswering.from_pretrained("wesley7137/BlipFinetune-ScienceQA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 132e9408e483e14a073f018ce8bc895416f4e7a66dd96abb847abb467892f4b2
- Size of remote file:
- 1.54 GB
- SHA256:
- 84f9f62ee08293dd5b8426f1a23d1ab89de9d673bae0dd7aa28a11ad471deb2d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.