Instructions to use s3-tresio/blip-image-captioning-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s3-tresio/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="s3-tresio/blip-image-captioning-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("s3-tresio/blip-image-captioning-base") model = AutoModelForMultimodalLM.from_pretrained("s3-tresio/blip-image-captioning-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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chore: add word to blacklist
1f43406
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chore: refactor to support many replaces
4c9579d -
feat: filter out word, clean up code
ca00272
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chore: update model ref
0ec4306 -
chore: swap model to large
db46852 -
chore: update mode;l
20bc25a -
chore: change model
cabb5b4 -
feat: upgrade to larger model
39120fd
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chore: cleanup unused imports
d5262cc -
fix: update mechanism for getting image from url
8b43f32 -
fix: wrap with BytesIO
98baf2d -
feat: update support array or urls
7864f19 -
feat: add custom handler
8b8f635