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:
- 9f77aa91ab6c13cd79cbfe874d4159fb894e53d71fb3caa4f2993db8e207ef4e
- Size of remote file:
- 26.7 MB
- SHA256:
- 2745ec85df838622956cc66d73d5648ee6041e942160fbc2aa205922a63c2b9c
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