初始化项目，由ModelHub XC社区提供模型

Model: CMU-AIRe/TARS-7B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# TARS-7B
+
+## Overview
+
+**TARS-7B** is an open-source reasoning model trained for safety using **TARS**: *Training Adaptive Reasoners for Safety* introduced in the paper: [**Reasoning as an Adaptive Defense for Safety**](https://arxiv.org/abs/2507.00971), to facilitate the research of reasoning models for LLM safety. This model is trained using a mixing ratio of \\(\lambda = 0.5\\) between harmful and harmless prompts, starting from the base model [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
+
+TARS is a simple but effective online reinforcement learning (RL) method that trains models to **adaptively reason** for **low refusal** and **safe behavior**, using three key ingredients:
+
+### 🔑 Key Ingredients
+- **Ingredient 1:** Lightweight supervised fine-tuning (SFT) for diverse generations
+- **Ingredient 2:** Mixing in harmless prompts during RL training  
+- **Ingredient 3:** Decoupled reward model for better exploration  
+
+For full details, please check out our [paper](https://arxiv.org/pdf/2507.00971) or [blogpost](https://training-adaptive-reasoners-safety.github.io).
+
+---
+
+## 📖 Citation
+
+If you use **TARS-7B** in your work, please cite us:
+
+```bibtex
+@article{kim2025reasoning,
+  title={Reasoning as an Adaptive Defense for Safety},
+  author={Kim, Taeyoun and Tajwar, Fahim and Raghunathan, Aditi and Kumar, Aviral},
+  journal={arXiv preprint arXiv:2507.00971},
+  year={2025}
+}