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@@ -11,6 +11,11 @@ tags:
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  - qwen3-vl
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  library_name: transformers
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  pipeline_tag: video-text-to-text
 
 
 
 
 
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  ---
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  # TimeLens-8B
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  **TimeLens-8B** is an MLLM with state-of-the-art video temporal grounding performance among open-source models, finetuned from [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct). It is trained with carefully crafted RLVR (reinforcement learning with verifiable rewards) recipe proposed in our [paper](TODO), utilizing our high-quality VTG training dataset [TimeLens-100K](https://huggingface.co/datasets/TencentARC/TimeLens-100K).
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- ## Performance
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  TimeLens-8B achieves state-of-the-art video temporal grounding performance among open-source models:
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@@ -122,7 +127,8 @@ Install the following packages:
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  ```bash
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  pip install transformers==4.57.1 accelerate==1.6.0 torch==2.6.0 torchvision==0.21.0
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  pip install qwen-vl-utils[decord]==0.0.14
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- pip install flash-attn==2.7.4.post1 --no-build-isolation --no-cache-dir # Flash-Attention 2 to speed up generation
 
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  ```
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  Using 🤗Transformers for Inference:
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  ```bibtex
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  TODO
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- ```
 
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  - qwen3-vl
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  library_name: transformers
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  pipeline_tag: video-text-to-text
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+ datasets:
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+ - TencentARC/TimeLens-100K
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+ - TencentARC/TimeLens-Bench
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+ base_model:
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+ - Qwen/Qwen3-VL-8B-Instruct
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  ---
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  # TimeLens-8B
 
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  **TimeLens-8B** is an MLLM with state-of-the-art video temporal grounding performance among open-source models, finetuned from [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct). It is trained with carefully crafted RLVR (reinforcement learning with verifiable rewards) recipe proposed in our [paper](TODO), utilizing our high-quality VTG training dataset [TimeLens-100K](https://huggingface.co/datasets/TencentARC/TimeLens-100K).
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+ ## 📊 Performance
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  TimeLens-8B achieves state-of-the-art video temporal grounding performance among open-source models:
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  ```bash
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  pip install transformers==4.57.1 accelerate==1.6.0 torch==2.6.0 torchvision==0.21.0
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  pip install qwen-vl-utils[decord]==0.0.14
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+ # use Flash-Attention 2 to speed up generation
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+ pip install flash-attn==2.7.4.post1 --no-build-isolation --no-cache-dir
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  ```
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  Using 🤗Transformers for Inference:
 
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  ```bibtex
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  TODO
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+ ```