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")# Load model directly from transformers import SmallCap model = SmallCap.from_pretrained("Yova/SmallCap7M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download coco_index_captions.json from Yova/SmallCap7M: direct link, hf CLI and curl.
- Browser
- Download file 31.3 MB
-
https://huggingface.co/Yova/SmallCap7M/resolve/main/coco_index_captions.json
- Command line
-
hf download hf://Yova/SmallCap7M/coco_index_captions.json
-
curl -L -o coco_index_captions.json https://huggingface.co/Yova/SmallCap7M/resolve/main/coco_index_captions.json
31.3 MB
- Xet hash:
- f99df7fb7c4e5562b727386b23452f2bba292dd798d966ae1db5cffc09cbd3b9
- Size of remote file:
- 31.3 MB
- SHA256:
- 5cf206a68ae18a10667928fd916d077a3374f361d83e10a5482d2f71ada041a6
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