Feature Extraction
Transformers
Safetensors
videojev
video
video-question-answering
temporal-grounding
custom_code
Instructions to use ZhuYuhan/VideoJev-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZhuYuhan/VideoJev-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ZhuYuhan/VideoJev-4B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZhuYuhan/VideoJev-4B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
4 days ago
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Update VideoJev BibTeX citation
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7 days ago
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Add arXiv paper link and official BibTeX citation
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9 days ago
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Update model links after transfer to ZhuYuhan
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Use fixed 256K QA defaults and document HF checkpoint evaluation
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Default grounding threshold to 0.25 with per-call overrides
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Run demo at 2 FPS with max_frames=2048
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Download demo from the author account
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Use real-video examples and return probabilities without logits
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Shorten usage guide and keep task settings and complete output
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Show logits and probabilities in output examples
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Remove benchmark comparison note from model card
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Remove benchmark data JSON and weight memory note
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Add per-query latency to benchmark figures
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Add benchmark figures and simplify model card
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10 days ago
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Document grounding and QA output examples and field meanings
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Add files using upload-large-folder tool
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