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 vizwiz_index from Yova/SmallCap7M: direct link, hf CLI and curl.
- Browser
- Download file 480 MB
-
https://huggingface.co/Yova/SmallCap7M/resolve/d22debd9dc63983a6574d38ec77aa3457fde1453/vizwiz_index
- Command line
-
hf download hf://Yova/SmallCap7M@d22debd9dc63983a6574d38ec77aa3457fde1453/vizwiz_index
-
curl -L -o vizwiz_index https://huggingface.co/Yova/SmallCap7M/resolve/d22debd9dc63983a6574d38ec77aa3457fde1453/vizwiz_index
480 MB
- Xet hash:
- 764f531c6edbdcc1536aee29e49c3cb492a0e59cddd4e7598212fd409ce6f429
- Size of remote file:
- 480 MB
- SHA256:
- 7cb537e3201eb2004f7ba1096d7b49ad9df370243ce37db4df4cd1c5143174e0
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