Instructions to use yl4300/TAU-R1-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yl4300/TAU-R1-Classifier with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("yl4300/TAU-R1-Classifier") model = AutoModelForMultimodalLM.from_pretrained("yl4300/TAU-R1-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add library name and pipeline tags, move paper link to markdown
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by nielsr HF Staff - opened
README.md
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license: mit
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license: mit
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library_name: transformers
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pipeline_tag: video-text-to-text
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# TAU-R1
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This repository contains the model presented in [TAU-R1: Visual Language Model for Traffic Anomaly Understanding](https://huggingface.co/papers/2603.19098).
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TAU-R1 is a two-layer vision-language framework for Traffic Anomaly Understanding (TAU). The first layer is a lightweight anomaly classifier that performs coarse anomaly categorisation, while the second layer is a larger anomaly reasoner that generates detailed event summaries. It is trained with a two-stage strategy consisting of decomposed-QA-enhanced supervised fine-tuning followed by TAU-GRPO, a GRPO-based post-training method with TAU-specific reward functions.
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Code: https://github.com/siri-rouser/TAU-R1.
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