Instructions to use ngocson2002/vivqa-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ngocson2002/vivqa-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ngocson2002/vivqa-model", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ngocson2002/vivqa-model", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_vivqa.py
Browse files- modeling_vivqa.py +1 -1
modeling_vivqa.py
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@@ -140,7 +140,7 @@ class BEiT3Wrapper(PreTrainedModel):
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def __init__(self, args, **kwargs):
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super().__init__(args)
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self.beit3 = ViVQABEiT3(args)
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-
self.apply(self._init_weights)
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def fix_init_weight(self):
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def rescale(param, layer_id):
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def __init__(self, args, **kwargs):
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super().__init__(args)
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self.beit3 = ViVQABEiT3(args)
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+
# self.apply(self._init_weights)
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def fix_init_weight(self):
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def rescale(param, layer_id):
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