Visual Question Answering
Transformers
Safetensors
English
videollama3_qwen2
text-generation
multi-modal
large-language-model
video-language-model
custom_code
Instructions to use DAMO-NLP-SG/VideoLLaMA3-7B-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DAMO-NLP-SG/VideoLLaMA3-7B-Image with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="DAMO-NLP-SG/VideoLLaMA3-7B-Image", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/VideoLLaMA3-7B-Image", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_videollama3.py
Browse files- modeling_videollama3.py +1 -1
modeling_videollama3.py
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@@ -286,7 +286,7 @@ class Videollama3MetaForCausalLM(ABC):
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# 2. embed visual tokens
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batched_num_patches = grid_sizes.prod(dim=1).div(merge_sizes ** 2).long()
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mm_features = self.encode_images(pixel_values, grid_sizes, merge_sizes)
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mm_features = self._get_valid_visual_tokens(mm_features, batched_num_patches, modals)
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compression_mask = self._get_compression_mask(
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# 2. embed visual tokens
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batched_num_patches = grid_sizes.prod(dim=1).div(merge_sizes ** 2).long()
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mm_features = self.encode_images(pixel_values, grid_sizes, merge_sizes).to(input_ids.device)
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mm_features = self._get_valid_visual_tokens(mm_features, batched_num_patches, modals)
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compression_mask = self._get_compression_mask(
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