Quantized Llama-3.1-8B Performance in CWE Detection on Big-Vul Benchmark

Large Language Models (LLMs) have demonstrated significant capabilities in understanding and analyzing code for security vulnerabilities, such as Common Weakness Enumerations (CWEs). However, their reliance on cloud infrastructure and substantial computational requirements pose challenges for analyzing sensitive or proprietary codebases due to privacy concerns and inference costs. This work explores the potential of Small Language Models (SLMs) as a viable alternative for accurate, on-premise vulnerability detection. We investigated whether a 350-million parameter pre-trained code model (codeg

Research goal: What is the comparative performance of quantized Llama-3.1-8B against full-precision variants on CWE detection tasks within the Big-Vul benchmark under varying context lengths?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.4/10.