Video-Text-to-Text
Transformers
Safetensors
English
qwen3_vl
image-text-to-text
video-grounding
temporal-grounding
video-understanding
qwen3-vl
Eval Results
Instructions to use TencentARC/TimeLens-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TencentARC/TimeLens-8B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("TencentARC/TimeLens-8B") model = AutoModelForMultimodalLM.from_pretrained("TencentARC/TimeLens-8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add OmniCompass evaluation results
Browse filesAdd the OmniCompass benchmark authors' published results for TimeLens-8B on omnicompass_vl. Each value is Overall Cross-Specification Consistency Score on a 0-100 scale; higher is better.
Source: https://omnicompass.netlify.app/#leaderboard
Benchmark: https://huggingface.co/datasets/VCLab-PolyU/OmniCompass
The YAML preserves the published one-decimal scores and records frame counts and source attribution. It adds evaluation metadata only and makes no HF verified claim.
.eval_results/omnicompass.yaml
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- dataset:
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id: VCLab-PolyU/OmniCompass
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task_id: omnicompass_vl
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value: 23.6
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source:
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url: https://omnicompass.netlify.app/#leaderboard
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name: OmniCompass official results
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user: VCLab-PolyU
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notes: OmniCompass-VL; Overall CS on a 0-100 scale, higher is better. 32 video frames;
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paired Text and Text+Image; TG, SG and QA. Imported from the benchmark authors'
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published results, rounded to one decimal. Evaluation settings and model-specific
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limits follow the cited source. This submission does not claim HF verified status.
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