Instructions to use NhatDFO/sf_blip2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NhatDFO/sf_blip2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="NhatDFO/sf_blip2")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("NhatDFO/sf_blip2") model = AutoModelForVisualQuestionAnswering.from_pretrained("NhatDFO/sf_blip2") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:dc6bd609d353d1f3839a91ebe1419b068ff48c303bd0182592a9ab7ab2ef029e
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size 4010653792
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