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
Update config.json
Browse files- config.json +1 -0
config.json
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"chunk_size_feed_forward": 0,
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"cross_attention_hidden_size": null,
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"cross_attention_reduce_factor": 4,
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"decoder_start_token_id": null,
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"diversity_penalty": 0.0,
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"do_sample": false,
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"chunk_size_feed_forward": 0,
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"cross_attention_hidden_size": null,
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"cross_attention_reduce_factor": 4,
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"encoder_hidden_size": 768,
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"decoder_start_token_id": null,
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"diversity_penalty": 0.0,
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"do_sample": false,
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