---
license: apache-2.0
datasets:
- siddhi-lipare/RoadTones
language:
- en
base_model:
- Qwen/Qwen3-VL-8B-Instruct-FP8
pipeline_tag: video-text-to-text
---

# RoadTones-VL-CoT

This model accompanies the paper **RoadTones: Tone Controllable Text Generation from Road Event Videos**

## Model Summary

`RoadTones-VL-CoT` is an open-source large multimodal model with superior tone-controlled road video captioning capabilities. Built on the foundation of `Qwen3-VL-8B-Instruct`, it has been finetuned on [RoadTones-51k](https://huggingface.co/datasets/siddhi-lipare/RoadTones) dataset with the Chain-of-Thought (CoT) intermediate drafts as well for better interpretability. Evaluated on the RoadTones-Eval Metrics, Its performance is on par with the best benchmarked model (Gemini-2.5-pro) while displaying superior Tone Adherance, thereby demonstrating the RoadTones-51K dataset's capability in improving the tone-controllability for text generation in VideoLLMs.

For further details, please refer to the following resources:
- 🪐 Project Page: https://roadtones.github.io
- 📦 Dataset: https://huggingface.co/datasets/siddhi-lipare/RoadTones
- 💻 Code: https://github.com/roadtones/roadtones
- 📰 Paper: https://arxiv.org/abs/2605.21411

## Citation

```bibtex
@misc{parikh2026roadtonestonecontrollabletext,
      title={RoadTones: Tone Controllable Text Generation from Road Event Videos}, 
      author={Chirag Parikh and Siddhi Pravin Lipare and Ravi Kiran Sarvadevabhatla},
      year={2026},
      eprint={2605.21411},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2605.21411}, 
}
```