Video-Text-to-Text
Transformers
Safetensors
qwen3_vl
image-text-to-text
video-retrieval
temporal-grounding
videosearch-r1
Instructions to use VideoSearchR1/charades-stage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VideoSearchR1/charades-stage2 with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("VideoSearchR1/charades-stage2") model = AutoModelForMultimodalLM.from_pretrained("VideoSearchR1/charades-stage2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
base_model: Qwen/Qwen3-VL-4B-Instruct
library_name: transformers
license: apache-2.0
pipeline_tag: video-text-to-text
tags:
- video-retrieval
- temporal-grounding
- videosearch-r1
VideoSearch-R1 Charades-STA Stage 2
This is the Stage 2 VideoSearch-R1 checkpoint trained for Charades-STA, presented in the paper VideoSearch-R1: Iterative Video Retrieval and Reasoning via Soft Query Refinement.
- Project Page: https://mlvlab.github.io/VideoSearch-R1/
- Repository: https://github.com/mlvlab/VideoSearch-R1
Usage
Use with the VideoSearch-R1 codebase:
bash scripts/data_construct/download_preextracted_data.bash charades
EVAL_GPUS=0 bash scripts/inference/inference.bash charades --checkpoint VideoSearchR1/charades-stage2