Instructions to use getZuma/image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use getZuma/image-captioning with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="getZuma/image-captioning")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("getZuma/image-captioning") model = AutoModelForMultimodalLM.from_pretrained("getZuma/image-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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spell fix
317eeaa -
Updated readme file
16a8334 -
Fixed variable error.
4ad5631 -
rollback to previous code
db1d42e -
removed model to device
removed model to device98f4fce -
Updated with new code
56a6bf1 -
removed devices
eca2bf8 -
Moved to 16 bit precision
7ab9f15 -
fixed Cuda Issue
2ae7815 -
Added self in the method call
d231d89 -
Added more loggers
f27c209 -
Updated handler file
e454733
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Added new handler
Added new handler7c90bcf -
Duplicate from Salesforce/blip2-opt-2.7b
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>28b2957