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/VideoLLaMA3-2B-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DAMO-NLP-SG/VideoLLaMA3-2B-Image with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="DAMO-NLP-SG/VideoLLaMA3-2B-Image", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/VideoLLaMA3-2B-Image", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update image_processing_videollama3.py
Browse files
image_processing_videollama3.py
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class Videollama3ImageProcessor(BaseImageProcessor):
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r"""
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Constructs a
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Args:
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do_resize (`bool`, *optional*, defaults to `True`):
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class Videollama3ImageProcessor(BaseImageProcessor):
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r"""
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Constructs a VideoLLaMA3 image processor that dynamically resizes images based on the original images.
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Args:
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do_resize (`bool`, *optional*, defaults to `True`):
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