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-00001-of-00004.safetensors from TIGER-Lab/VideoScore-Qwen2-VL: direct link, hf CLI and curl.
- Browser
- Download file 4.97 GB
-
https://huggingface.co/TIGER-Lab/VideoScore-Qwen2-VL/resolve/main/model-00001-of-00004.safetensors
- Command line
-
hf download hf://TIGER-Lab/VideoScore-Qwen2-VL/model-00001-of-00004.safetensors
-
curl -L -o model-00001-of-00004.safetensors https://huggingface.co/TIGER-Lab/VideoScore-Qwen2-VL/resolve/main/model-00001-of-00004.safetensors
4.97 GB
- Xet hash:
- dae446d25ea4b70edf5e5f71758754f413c11037b65f20bc18916400af7990f2
- Size of remote file:
- 4.97 GB
- SHA256:
- a4e9f765c15b9c487be76d648a908fc512cf019d3171ddc8eb6e42395744b616
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