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 processing_videollama3.py
Browse files
processing_videollama3.py
CHANGED
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@@ -308,7 +308,7 @@ class Videollama3Qwen2Processor(ProcessorMixin):
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if isinstance(image_path, list):
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images = [load_single_image(f) for f in image_path]
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elif isinstance(image_path, str) and os.path.isdir(image_path):
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-
images = Image.open(os.path.join(image_path, f)).convert('RGB') for f in sorted(os.listdir(image_path))]
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else:
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images = [load_single_image(image_path)]
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return images
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if isinstance(image_path, list):
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images = [load_single_image(f) for f in image_path]
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elif isinstance(image_path, str) and os.path.isdir(image_path):
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+
images = [Image.open(os.path.join(image_path, f)).convert('RGB') for f in sorted(os.listdir(image_path))]
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else:
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images = [load_single_image(image_path)]
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return images
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