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
- Xet hash:
- 7540f3354088f51a064d647bf0372bb34bf2ce4f805c7a0b791517607651f0da
- Size of remote file:
- 81.4 MB
- SHA256:
- ea5be27e0f8ed0a9dc9e5c39c5f7c071340297a1e915790d2b549e39229c5f69
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