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:
- abb8026f2be01a6acdacf0057cb42d169b6cb40ebd40719bda8a92059f843d8c
- Size of remote file:
- 594 MB
- SHA256:
- 19e7f90b8d19bff57c989ed33a3fc813dbb9f391ee441d36b70cfb0a8bd98858
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