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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 051b1a1ba13247a8bbc2053293d2af3af6fd828e5506f9f6d7f9d005c9e3168e
- Size of remote file:
- 30.1 GB
- SHA256:
- 2021b49ec6e45af772787825f5cc61d7cb0bfc54eae04364350a7c5ecc2d6300
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