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 generation_config.json from TIGER-Lab/VideoScore-Qwen2-VL: direct link, hf CLI and curl.
- Browser
- Download file 317 Bytes
-
https://huggingface.co/TIGER-Lab/VideoScore-Qwen2-VL/resolve/main/generation_config.json
- Command line
-
hf download hf://TIGER-Lab/VideoScore-Qwen2-VL/generation_config.json
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curl -L -o generation_config.json https://huggingface.co/TIGER-Lab/VideoScore-Qwen2-VL/resolve/main/generation_config.json
317 Bytes
| { | |
| "attn_implementation": "flash_attention_2", | |
| "bos_token_id": 151643, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 151645, | |
| 151643 | |
| ], | |
| "num_labels": 5, | |
| "pad_token_id": 151643, | |
| "problem_type": "regression", | |
| "temperature": 0.01, | |
| "top_k": 1, | |
| "top_p": 0.001, | |
| "transformers_version": "4.45.0.dev0" | |
| } | |