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 ln_ade20k_index_captions.json from Yova/SmallCap7M: direct link, hf CLI and curl.
- Browser
- Download file 1.04 MB
-
https://huggingface.co/Yova/SmallCap7M/resolve/main/ln_ade20k_index_captions.json
- Command line
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hf download hf://Yova/SmallCap7M/ln_ade20k_index_captions.json
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curl -L -o ln_ade20k_index_captions.json https://huggingface.co/Yova/SmallCap7M/resolve/main/ln_ade20k_index_captions.json
1.04 MB
File too large to display, you can check the raw version instead.