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  base_model:
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  - huihui-ai/Qwen3-8B-abliterated
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  ---
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- # 🤖 StrikeGPT-R1-Zero: 网络安全渗透领域推理模型
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-
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- ## 🚀 模型简介
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- **StrikeGPT-R1-Zero** 是基于 **Qwen3** 进行黑盒蒸馏的专家模型,其教师模型为 DeepSeek-R1,涵盖:
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- 🔒 AI安全 | 🛡️ API安全 | 📱 APP安全 | 🕵️ APT | 🚩 CTF
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- 🏭 ICS安全 | 💻 渗透测试ALL | ☁️ 云上安全 | 📜 代码审计
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- 🦠 免杀 | 🌐 内网安全 | 💾 电子取证 | ₿ 区块链安全 | 🕳️ 溯源反制 | 🌍 物联网(IoT)安全<br>
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- 🚨 应急响应 | 🚗 整车安全 | 👥 社会工程学 | 💼 渗透测试面试
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- ### 👉 [点击访问可交互式详细数据分布图](https://bouquets-ai.github.io/StrikeGPT-R1-Zero/WEB)
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- ### 🌟 模型亮点
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- - 🧩采用**思维链(CoT)推理数据**优化模型逻辑能力,显著提升在漏洞分析等复杂任务的表现
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- - 💪Base模型采用Qwen3相较于Distill-Llama更适合中国宝宝体制
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- - ⚠️**无道德限制**在特定领域的学术研究有不一样的表现(请在符合当地法律的情况下使用)
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- - ✨特定情况下如断网状态下的**网络安全大赛**,相较于本地RAG形式StrikeGPT-R1-Zero逻辑推理能力更强,在复杂任务处理方面表现更佳。
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-
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- ## 📊 数据分布
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- ![data](https://github.com/user-attachments/assets/4d19d48d-67bb-4b05-8ce9-2000b6afa12e)
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-
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-
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- ## 🛠️模型部署
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- ### 通过ollama进行部署
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- `ollama run hf.co/Bouquets/StrikeGPT-R1-Zero-8B-Q4_K_M-GGUF:Q4_K_M`
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-
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- 经过量化后自我认知有点问题
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-
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- ![image](https://github.com/user-attachments/assets/3989ea09-d581-49fb-9938-01b93e0beb91)
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-
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-
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- ## 🎯 核心能力展示&对比(原模型有道德限制就不做比较,简单比较SecGPT-7B模型【大佬写的评估脚本我改不来/(ㄒoㄒ)/~~】)
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- ![image](https://github.com/user-attachments/assets/8166a1d3-c69f-4b8a-821f-0dd83dcd4544)
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-
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- ### CTF
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- ![image](https://github.com/user-attachments/assets/e6552b0b-521f-4d3f-8ba1-b9a3ce136d65)
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- ![image](https://github.com/user-attachments/assets/df55e964-0bc3-45a9-97a6-625ea9d086fe)
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-
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- #### Reverse Engineering
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- ![image](https://github.com/user-attachments/assets/18f83228-9fa3-44ec-8403-389371de7e88)
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- ![image](https://github.com/user-attachments/assets/4b13ba4a-10ff-45dd-9f0b-80d64327df59)
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- #### PWN
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- ![image](https://github.com/user-attachments/assets/50108ebf-0979-46f6-9c01-47d4362e6832)
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- ![image](https://github.com/user-attachments/assets/af44b4a6-ea34-4247-a949-d8c59c87d929)
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- #### Web
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- ![image](https://github.com/user-attachments/assets/4e73c0b2-de94-45de-813d-0b4c5d9cf263)
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- ![image](https://github.com/user-attachments/assets/8847903c-d68d-47d7-ab15-a076401b0ca2)
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- #### Crypto
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- ![image](https://github.com/user-attachments/assets/8d2266d1-1282-425c-b89d-b83f80a30314)
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- ![image](https://github.com/user-attachments/assets/991b84f5-600b-4646-aac5-2b1c4d1712c1)
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-
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- #### Misc
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- ![image](https://github.com/user-attachments/assets/dcdeaa59-c15d-4349-ac9f-642008c12178)
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- ![image](https://github.com/user-attachments/assets/af240992-faca-4d5c-be9e-513f727543cf)
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- #### Blockchain
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- ![image](https://github.com/user-attachments/assets/62f57e7e-8add-40e6-a532-bae07887ba1e)
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- ![image](https://github.com/user-attachments/assets/4302694a-89a6-4117-a568-79f8c74bb815)
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- #### IoT
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- ![image](https://github.com/user-attachments/assets/d30a620f-f5e7-473c-a2f5-2ae171479e3f)
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- ![image](https://github.com/user-attachments/assets/bb3288b4-fa47-4265-9a30-8fdd62b1e651)
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-
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- ### 内网安全
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- ![image](https://github.com/user-attachments/assets/02fba088-9419-47ec-9072-de9a362a4e08)
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- ![image](https://github.com/user-attachments/assets/05e9aef3-690f-4608-998c-8715e1a90e59)
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-
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- ### 社工
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- ![image](https://github.com/user-attachments/assets/6e1eb9ec-1bf5-4bc2-acdf-c5b004b58f6e)
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- ![image](https://github.com/user-attachments/assets/f0c93222-56e6-4253-b6bb-3eeb8ec7d9cf)
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-
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- ### 代码编写
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- ![image](https://github.com/user-attachments/assets/6e037fff-e46b-42d5-997d-559fb300aba0)
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- ![image](https://github.com/user-attachments/assets/e8c1c0fd-16af-46e1-8b7b-57947145f545)
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-
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- ### 代码审计(联动项目DeepSeekSelfTool)
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- ![image](https://github.com/user-attachments/assets/c7dc4b66-379d-4c57-aaf2-3d4d73d1484c)
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-
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-
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- ## 📈 实验数据走势图
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- 有些许梯度爆炸,总体问题不大
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- ![image](https://github.com/user-attachments/assets/a3fa3676-9f07-47ea-9029-ec0d56fdc989)
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-
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- ## 💰 训练成本
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- - **DeepSeek-R1 API调用费用**: ¥450 (均在打折时调用,正常调用价格在¥1800)
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- - **服务器开销**: ¥4?0
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- - **电子资源**: ¥??
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- ![image](https://github.com/user-attachments/assets/8e23b5b6-24d9-47c3-b54f-ffa22ec68a83)
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-
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- ## ⚖️ 使用须知
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- > 本模型仅供**合法安全研究**与**教育用途**。使用者需遵守所在地法律法规,开发者不对滥用行为负责。<br>
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- > 提示:使用即表示您同意本声明
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-
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- 💡 **提示**: 模型可能存在幻觉或知识盲区,关键场景请交叉验证!
 
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  base_model:
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  - huihui-ai/Qwen3-8B-abliterated
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  ---
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+ # 🤖 StrikeGPT-R1-Zero: Cybersecurity Penetration Reasoning Model
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+
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+ ## 🚀 Model Introduction
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+ **StrikeGPT-R1-Zero** is an expert model based on **Qwen3** through black-box distillation, with DeepSeek-R1 as its teacher model. It covers:
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+ 🔒 AI Security | 🛡️ API Security | 📱 APP Security | 🕵️ APT | 🚩 CTF
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+ 🏭 ICS Security | 💻 Penetration Testing ALL | ☁️ Cloud Security | 📜 Code Audit
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+ 🦠 Antivirus Evasion | 🌐 Internal Network Security | 💾 Digital Forensics | ₿ Blockchain Security | 🕳️ Traceability & Countermeasures | 🌍 IoT Security
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+ 🚨 Emergency Response | 🚗 Vehicle Security | 👥 Social Engineering | 💼 Penetration Testing Interviews
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+
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+ ### 👉 [Click to Access Interactive Detailed Data Distribution](https://bouquets-ai.github.io/StrikeGPT-R1-Zero/WEB)
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+ ### 🌟 Highlights
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+ - 🧩 Utilizes **Chain-of-Thought (CoT) reasoning data** to optimize the model's logical capabilities, significantly improving performance in complex tasks such as vulnerability analysis.
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+ - 💪 The base model uses Qwen3, which is more suitable for Chinese users compared to Distill-Llama.
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+ - ⚠️ **No ethical restrictions**—demonstrates unique performance in specific academic research areas (use in compliance with local laws).
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+ - ✨ In specific scenarios, such as **offline cybersecurity competitions**, StrikeGPT-R1-Zero exhibits stronger logical reasoning capabilities compared to local RAG solutions, performing better in complex task handling.
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+
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+ ## 📊 Data Distribution
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+ ![data](https://github.com/user-attachments/assets/4d19d48d-67bb-4b05-8ce9-2000b6afa12e)
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+
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+ ## 🛠️ Model Deployment
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+ ### Deploy via Ollama
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+ `ollama run hf.co/Bouquets/StrikeGPT-R1-Zero-8B-Q4_K_M-GGUF:Q4_K_M`
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+
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+ After quantization, there are slight self-awareness issues.
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+
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+ ![image](https://github.com/user-attachments/assets/3989ea09-d581-49fb-9938-01b93e0beb91)
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+
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+ ## 🎯 Core Capabilities Showcase & Comparison (The original model has ethical restrictions, so no direct comparison is made. A simple comparison with the SecGPT-7B model is provided instead.)
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+ ![image](https://github.com/user-attachments/assets/8166a1d3-c69f-4b8a-821f-0dd83dcd4544)
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+
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+ ### CTF
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+ ![image](https://github.com/user-attachments/assets/e6552b0b-521f-4d3f-8ba1-b9a3ce136d65)
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+ ![image](https://github.com/user-attachments/assets/df55e964-0bc3-45a9-97a6-625ea9d086fe)
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+
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+ #### Reverse Engineering
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+ ![image](https://github.com/user-attachments/assets/18f83228-9fa3-44ec-8403-389371de7e88)
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+ ![image](https://github.com/user-attachments/assets/4b13ba4a-10ff-45dd-9f0b-80d64327df59)
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+
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+ #### PWN
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+ ![image](https://github.com/user-attachments/assets/50108ebf-0979-46f6-9c01-47d4362e6832)
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+ ![image](https://github.com/user-attachments/assets/af44b4a6-ea34-4247-a949-d8c59c87d929)
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+
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+ #### Web
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+ ![image](https://github.com/user-attachments/assets/4e73c0b2-de94-45de-813d-0b4c5d9cf263)
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+ ![image](https://github.com/user-attachments/assets/8847903c-d68d-47d7-ab15-a076401b0ca2)
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+
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+ #### Crypto
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+ ![image](https://github.com/user-attachments/assets/8d2266d1-1282-425c-b89d-b83f80a30314)
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+ ![image](https://github.com/user-attachments/assets/991b84f5-600b-4646-aac5-2b1c4d1712c1)
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+
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+ #### Misc
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+ ![image](https://github.com/user-attachments/assets/dcdeaa59-c15d-4349-ac9f-642008c12178)
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+ ![image](https://github.com/user-attachments/assets/af240992-faca-4d5c-be9e-513f727543cf)
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+
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+ #### Blockchain
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+ ![image](https://github.com/user-attachments/assets/62f57e7e-8add-40e6-a532-bae07887ba1e)
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+ ![image](https://github.com/user-attachments/assets/4302694a-89a6-4117-a568-79f8c74bb815)
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+
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+ #### IoT
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+ ![image](https://github.com/user-attachments/assets/d30a620f-f5e7-473c-a2f5-2ae171479e3f)
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+ ![image](https://github.com/user-attachments/assets/bb3288b4-fa47-4265-9a30-8fdd62b1e651)
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+
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+ ### Internal Network Security
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+ ![image](https://github.com/user-attachments/assets/02fba088-9419-47ec-9072-de9a362a4e08)
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+ ![image](https://github.com/user-attachments/assets/05e9aef3-690f-4608-998c-8715e1a90e59)
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+
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+ ### Social Engineering
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+ ![image](https://github.com/user-attachments/assets/6e1eb9ec-1bf5-4bc2-acdf-c5b004b58f6e)
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+ ![image](https://github.com/user-attachments/assets/f0c93222-56e6-4253-b6bb-3eeb8ec7d9cf)
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+
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+ ### Code Writing
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+ ![image](https://github.com/user-attachments/assets/6e037fff-e46b-42d5-997d-559fb300aba0)
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+ ![image](https://github.com/user-attachments/assets/e8c1c0fd-16af-46e1-8b7b-57947145f545)
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+
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+ ### Code Audit (Linked with DeepSeekSelfTool Project)
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+ ![image](https://github.com/user-attachments/assets/c7dc4b66-379d-4c57-aaf2-3d4d73d1484c)
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+
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+ ## 📈 Experimental Data Trends
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+ Some gradient explosion observed, but overall manageable.
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+ ![image](https://github.com/user-attachments/assets/a3fa3676-9f07-47ea-9029-ec0d56fdc989)
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+
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+ ## 💰 Training Costs
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+ - **DeepSeek-R1 API Call Costs**: ¥450 (all called during discounts; normal price would be ¥1800)
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+ - **Server Expenses**: ¥4?0
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+ - **Electronic Resources**: ¥??
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+ ![image](https://github.com/user-attachments/assets/8e23b5b6-24d9-47c3-b54f-ffa22ec68a83)
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+
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+ ## ⚖️ Usage Notice
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+ > This model is intended **only for legal security research and educational purposes**. Users must comply with local laws and regulations. The developers are not responsible for misuse.
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+ > **Note**: By using this model, you agree to this disclaimer.
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+
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+ 💡 **Tip**: The model may exhibit hallucinations or knowledge gaps. Cross-validate critical scenarios!
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