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
Commit ·
0634956
1
Parent(s): 142b2b1
Update model
Browse files- model.safetensors +1 -1
- modeling_vivqa.py +1 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 4911309508
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version https://git-lfs.github.com/spec/v1
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oid sha256:a73ebbde41b8a49474b4ebabda8995e679dc03471f724d6997619b15a01ce99c
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size 4911309508
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modeling_vivqa.py
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@@ -89,6 +89,7 @@ class ViVQABEiT3(PreTrainedModel):
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self.vision_embed = Blip2EfficientExtractor()
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for param in self.vision_embed.parameters():
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param.requires_grad = False
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self.linear = nn.Linear(1024, 768)
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self.vision_embed = Blip2EfficientExtractor()
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for param in self.vision_embed.parameters():
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param.requires_grad = False
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self.linear = nn.Linear(1024, 768)
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