Video-Text-to-Text
Transformers
Safetensors
qwen3_vl
image-text-to-text
video-retrieval
temporal-grounding
videosearch-r1
Instructions to use VideoSearchR1/didemo-stage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VideoSearchR1/didemo-stage2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("VideoSearchR1/didemo-stage2") model = AutoModelForMultimodalLM.from_pretrained("VideoSearchR1/didemo-stage2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add video-text-to-text pipeline tag, link to paper, project page, and code
Verified signatureThis PR improves the model card for VideoSearch-R1 DiDeMo Stage 2 by: - Adding the `pipeline_tag: video-text-to-text` metadata to ensure it is discoverable under the correct category on the Hub. - Linking the model card to its research paper: [VideoSearch-R1: Iterative Video Retrieval and Reasoning via Soft Query Refinement](https://huggingface.co/papers/2607.00446). - Adding links to the official project page and GitHub repository. - Adding the BibTeX citation block.606f77e
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Update stage checkpoint model card
Verified signature38c4033
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Initial model release
seohyun8825a4d1f4d