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README.md

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+---
+license: apache-2.0
+language:
+- zh
+pipeline_tag: audio-text-to-text
+library_name: transformers
+tags:
+- telecom-fraud
+- audio-text
+- qwen2-audio
+- chinese
+- speech-understanding
+- supervised-fine-tuning
+- arxiv:2503.24115
+---
+
+# AntiFraud-SFT
+
+AntiFraud-SFT is a supervised fine-tuned audio-text fraud detection model built on top of Qwen2-Audio for Chinese telecom fraud analysis.
+
+## Overview
+
+This model is trained on the TeleAntiFraud-28k dataset and is designed for:
+
+- telecom fraud detection from call audio
+- scene understanding from audio-text conversational inputs
+- fraud-related reasoning over Chinese phone-call content
+
+The current release is intended as a research model checkpoint for reproduction and further study.
+
+## Related Resources
+
+- TeleAntiFraud dataset repository: https://github.com/JimmyMa99/TeleAntiFraud
+- TeleAntiFraud dataset on Hugging Face: https://huggingface.co/datasets/JimmyMa99/TeleAntiFraud
+- TeleAntiFraud dataset on ModelScope: https://www.modelscope.cn/datasets/JimmyMa99/TeleAntiFraud
+- TeleAntiFraud-28k paper: https://huggingface.co/papers/2503.24115
+- SAFE-QAQ code repository: https://github.com/Control-derek/SAFE-QAQ
+- SAFE-QAQ paper: https://arxiv.org/abs/2601.01392
+
+## Model Details
+
+- Base model: `Qwen/Qwen2-Audio-7B-Instruct`
+- Architecture: `Qwen2AudioForConditionalGeneration`
+- Framework: PyTorch
+- Weight format: `safetensors`
+- License: Apache License 2.0
+
+## Usage
+
+This repository contains the model weights and tokenizer / processor files required for inference with `transformers`.
+
+Example loading code:
+
+```python
+from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration
+
+model_id = "JimmyMa99/AntiFraud-SFT"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = Qwen2AudioForConditionalGeneration.from_pretrained(
+    model_id,
+    device_map="auto",
+)
+```
+
+## Notes
+
+- This release focuses on model weights for research and benchmarking.
+- For evaluation scripts and LM-as-judge utilities, see the `evaluation/` directory in the TeleAntiFraud repository.
+- For the end-to-end reinforcement-learning follow-up paper, see SAFE-QAQ.
+
+## Citation
+
+```bibtex
+@inproceedings{ma2025teleantifraud,
+  title={TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection},
+  author={Ma, Zhiming and Wang, Peidong and Huang, Minhua and Wang, Jinpeng and Wu, Kai and Lv, Xiangzhao and Pang, Yachun and Yang, Yin and Tang, Wenjie and Kang, Yuchen},
+  booktitle={Proceedings of the 33rd ACM International Conference on Multimedia},
+  pages={5853--5862},
+  year={2025}
+}
+
+@article{wang2026safe,
+  title={SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning},
+  author={Wang, Peidong and Ma, Zhiming and Dai, Xin and Liu, Yongkang and Feng, Shi and Yang, Xiaocui and Hu, Wenxing and Wang, Zhihao and Pan, Mingjun and Yuan, Li and others},
+  journal={arXiv preprint arXiv:2601.01392},
+  year={2026}
+}
+```