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_captions.json from Yova/SmallCap7M: direct link, hf CLI and curl.
- Browser
- Download file 7.59 MB
-
https://huggingface.co/Yova/SmallCap7M/resolve/main/vizwiz_index_captions.json
- Command line
-
hf download hf://Yova/SmallCap7M/vizwiz_index_captions.json
-
curl -L -o vizwiz_index_captions.json https://huggingface.co/Yova/SmallCap7M/resolve/main/vizwiz_index_captions.json
7.59 MB
- Xet hash:
- b8adcd1734f0538781604bc244f94091a3b82b6896cf4e3eee26b35d6c709667
- Size of remote file:
- 7.59 MB
- SHA256:
- 08793b63d70985c853e5f27d39fb0590f662eb611244f3cb2c5d794b66759ba9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.