Video-Text-to-Text
Transformers
Safetensors
qwen3_vl
image-text-to-text
video-retrieval
temporal-grounding
videosearch-r1
Instructions to use VideoSearchR1/activitynet-stage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VideoSearchR1/activitynet-stage2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("VideoSearchR1/activitynet-stage2") model = AutoModelForMultimodalLM.from_pretrained("VideoSearchR1/activitynet-stage2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from VideoSearchR1/activitynet-stage2: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/VideoSearchR1/activitynet-stage2/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://VideoSearchR1/activitynet-stage2@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/VideoSearchR1/activitynet-stage2/resolve/refs%2Fpr%2F1/tokenizer.json
11.4 MB
- Xet hash:
- 81ba189117386911cebccbc60d7c4ebc2634f47402d8b1d85d98b3ac6d2032b2
- Size of remote file:
- 11.4 MB
- SHA256:
- eda24b2b728f3383edd5ac02192ea21c77b021c338367b1542fa2cbe4c0f6d44
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