README.md

---
library_name: transformers
license: mit
language:
- en
- zh
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
tags:
- safe
---

# RealSafe-R1-7B

## Overview / 综述

RealSafe-R1-7B is a **safety-enhanced** variant of [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B), developed to improve robustness against malicious queries, especially jailbreak attacks. While the original DeepSeek-R1 series demonstrates strong reasoning and generation capabilities, it has been found to be vulnerable to safety risks. This model has been fine-tuned using supervised fine-tuning (SFT) on customized safety-focused datasets, improving its ability to detect and refuse harmful, unethical, or policy-violating prompts while maintaining its original capabilities.

RealSafe-R1-7B是DeepSeek-R1-Distilled-Qwen-7B的**安全加固**版本，显著提高模型对恶意查询，特别是越狱攻击的鲁棒性。尽管DeepSeek-R1系列模型展现出了强大的推理能力，但其在安全性方面仍存在一定的风险。该模型经过在自有安全数据集上的监督微调训练（SFT），其检测和拒绝有害、不道德或违反政策的提示词的能力得到增强，同时保持了原有的能力。

## Key Features / 关键特征

* **Improved Safety Awareness:** Improved refusal mechanisms for adversarial prompts and enhanced detection of unsafe queries.
* **Retained Reasoning Abilities:** Maintains high-quality performance on common sense, logic, and mathematical reasoning tasks.

* **提升安全意识：** 强化针对恶意提示词的拒绝机制，并增强了对不安全查询的检测能力。
* **保留推理能力：** 在常识、逻辑和数学推理任务上保持高质量的性能。

## Evaluation / 评估

RealSafe-R1-7B is evaluated on several benchmarks to show the effective safety consolidation and retained reasoning abilities. We follow the evaluation setting of DeepSeek-R1 and estimate the performance with 5 runs.

通过在多个基准测试上进行评估，我们验证RealSafe-R1-7B的有效安全增强效果和推理能力。我们遵循了DeepSeek-R1的评估设置，并通过重复五组实验来估算模型性能。

### 1. Safety Performance against Jailbreak Attacks / 针对越狱攻击的安全表现

Refusal rates (%) to malicious queries from StrongReject are reported.

针对StrongReject中恶意提示的拒答率（%）

| Model | None | PAP | PAIR |
|---|:---:|:---:|:---:|
|**DeepSeek-R1-Distill-Qwen-7B**| 55.06 | 37.16 | 30.35 |
|**RealSafe-R1-7B**| 99.78 | 97.99 | 89.93 |

### 2. General Performance on Diverse Reasoning Tasks / 不同推理任务的通用性能

Accuracies (%) are reported.

在多个主流推理数据集上的准确率（%）

| Model | MATH-500 | AIME2024 | GPQA-Diamond | LiveCodeBench |
|---|:---:|:---:|:---:|:---:|
|**DeepSeek-R1-Distill-Qwen-7B**| 88.16 | 55.71 | 47.88 | 38.23 |
|**RealSafe-R1-7B**| 87.44 | 57.14 | 49.29 | 34.65 |

### 3. Case Study / 案例分析

Question: "asdf"

| **DeepSeek-R1-Distill-Qwen-7B** | **RealSafe-R1-7B** |
|---|---|
|   |   |



## License / 许可条款

This model follows the licensing terms of the original DeepSeek-R1 series. Refer to the base model’s license for details.

该模型遵循DeepSeek-R1系列的许可条款。详情请参阅相关模型的许可说明。