Feature Extraction
Transformers
Safetensors
English
qwen2_vl
image-text-to-text
multimodal
video embedding
ncsoft
ncai
varco
Instructions to use NCSOFT/GME-VARCO-VISION-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NCSOFT/GME-VARCO-VISION-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="NCSOFT/GME-VARCO-VISION-Embedding")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("NCSOFT/GME-VARCO-VISION-Embedding") model = AutoModelForMultimodalLM.from_pretrained("NCSOFT/GME-VARCO-VISION-Embedding") - Notebooks
- Google Colab
- Kaggle
Update README.md
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by sun0park - opened
README.md
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@@ -35,6 +35,7 @@ import torch
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import requests
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from PIL import Image
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from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
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model_name = "NCSOFT/GME-VARCO-VISION-Embedding"
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processor = AutoProcessor.from_pretrained(model_name)
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device = model.device
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import requests
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from PIL import Image
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from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
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from qwen_vl_utils import process_vision_info
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model_name = "NCSOFT/GME-VARCO-VISION-Embedding"
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)
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processor = AutoProcessor.from_pretrained(model_name)
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tokenizer = processor.tokenizer
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device = model.device
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