Instructions to use Yova/SmallCap7M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yova/SmallCap7M 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="Yova/SmallCap7M")# pip install -U transformers accelerate # Load model directly from transformers import SmallCap model = SmallCap.from_pretrained("Yova/SmallCap7M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Upload 2 files
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Rename vizwiz.json to vizwiz_index_captions.json
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Rename vizwiz to vizwiz_index
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Rename ln_open_images.json to ln_open_images_index_captions.json
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Add datastores and captions
Indexes and corresponding captions37926c0
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Upload the vizwiz captions
The index captions of vizwiz770ae64 -
Upload vizwiz
The vizwiz datastore6bf3019
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upload coco index
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initial commit
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