Instructions to use TIGER-Lab/VideoScore-Qwen2-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TIGER-Lab/VideoScore-Qwen2-VL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TIGER-Lab/VideoScore-Qwen2-VL")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("TIGER-Lab/VideoScore-Qwen2-VL") model = AutoModelForSequenceClassification.from_pretrained("TIGER-Lab/VideoScore-Qwen2-VL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model-00003-of-00004.safetensors from TIGER-Lab/VideoScore-Qwen2-VL: direct link, hf CLI and curl.
- Browser
- Download file 4.93 GB
-
https://huggingface.co/TIGER-Lab/VideoScore-Qwen2-VL/resolve/main/model-00003-of-00004.safetensors
- Command line
-
hf download hf://TIGER-Lab/VideoScore-Qwen2-VL/model-00003-of-00004.safetensors
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curl -L -o model-00003-of-00004.safetensors https://huggingface.co/TIGER-Lab/VideoScore-Qwen2-VL/resolve/main/model-00003-of-00004.safetensors
4.93 GB
- Xet hash:
- ba4cec10fb7861961f0da5063d956ac51e1995e871fcf9f588a4bb5fd9988aed
- Size of remote file:
- 4.93 GB
- SHA256:
- 2b46c81ef5b4151de8699d21f9287924210daf86d237b53b3a5fe177f7d90c80
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