Instructions to use trunks/blip-image-captioning-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trunks/blip-image-captioning-base 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="trunks/blip-image-captioning-base")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("trunks/blip-image-captioning-base") model = AutoModelForMultimodalLM.from_pretrained("trunks/blip-image-captioning-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c6da0e56d243142e693e5011a12aeba88e781f55882a12873cc0fe2f5d3c963
- Size of remote file:
- 990 MB
- SHA256:
- bdb6594be124733bea0ba6d02fcb681487039e846094de4b89cea26ee8105fba
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