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 +2 -2
config.json
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@@ -3,7 +3,7 @@
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"SmallCap"
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],
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"decoder": {
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"_name_or_path": "
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"activation_function": "gelu_new",
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"add_cross_attention": true,
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"architectures": [
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"length_penalty": 1.0,
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"max_length": 20,
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"min_length": 0,
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"model_type": "
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"n_ctx": 1024,
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"n_embd": 768,
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"n_head": 12,
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"SmallCap"
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],
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"decoder": {
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"_name_or_path": "this_gpt2",
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"activation_function": "gelu_new",
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"add_cross_attention": true,
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"architectures": [
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"length_penalty": 1.0,
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"max_length": 20,
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"min_length": 0,
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"model_type": "this_gpt2",
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"n_ctx": 1024,
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"n_embd": 768,
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"n_head": 12,
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