Leopo1d/OpenVul-Qwen3-4B-SFT-ep5
Leopo1d/OpenVul-Qwen3-4B-SFT-ep5 is a 4 billion parameter Qwen3-based language model developed by Youpeng Li, Fuxun Yu, and Xinda Wang, fine-tuned for vulnerability detection in C/C++ code. It specializes in context-level security analysis, utilizing inter-procedural information rather than isolated functions. The model is trained on high-quality vulnerability reasoning Chain-of-Thought (CoT) data using rejection sampling to prevent ground-truth leakage and reasoning hallucinations. Its primary use is identifying security flaws and Common Weakness Enumeration (CWE) standards in code.
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OpenVul-Qwen3-4B-SFT-ep5: Vulnerability Detection Model
This model, developed by Youpeng Li, Fuxun Yu, and Xinda Wang, is a 4 billion parameter Qwen3-based language model specifically fine-tuned for vulnerability detection in C/C++ code. It leverages Chain-of-Thought (CoT) data for security reasoning and instruction-following capabilities.
Key Capabilities
- Context-level Vulnerability Detection: Unlike models that analyze isolated functions, OpenVul-Qwen3-4B-SFT-ep5 focuses on inter-procedural contexts, incorporating global variables, type definitions, and callee functions for a more comprehensive analysis.
- High-Quality Training: The model was trained using Rejection Sampling distilled from DeepSeek-R1-0528, a method chosen to prevent "ground-truth leakage" and reasoning hallucinations during the fine-tuning process.
- CWE Standard Focus: It is designed to identify security flaws and classify them according to Common Weakness Enumeration (CWE) standards.
Good For
- Developers and security researchers needing to identify security vulnerabilities in C/C++ codebases.
- Automated code review systems focused on security analysis.
- Environments requiring precise, evidence-based vulnerability analysis without speculation.
For optimal inference, the developers recommend using vLLM with specific parameters such as enable_thinking=True, n=8, repetition_penalty=1.0, temperature=0.6, top_p=0.95, top_k=20, and max_tokens=32768.