LLaMA 2 Fine-Tuning for Smart Contract Analysis (PEFT)

Frequently Asked Questions

01.01. What is parameter-efficient fine-tuning and why does it matter for smart contract analysis?

Parameter-efficient fine-tuning, or PEFT, is a family of methods that train a small set of new parameters while freezing the base model weights. LoRA and QLoRA are the dominant variants. They matter for smart contract analysis because they reduce a multi-GPU training run into a single A100 or H100 job, putting domain-adapted LLaMA 2 deployment within reach of small audit teams and Web3 protocol developers.

02.How does fine-tuned LLaMA 2 compare against Slither and Mythril for vulnerability detection?

Fine-tuned LLaMA 2 is complementary to deterministic analyzers, not a replacement. Slither and Mythril detect known vulnerability classes through static analysis and symbolic execution with high precision and recall on covered patterns. A fine-tuned LLM excels at flagging novel patterns, summarizing audit findings in natural language, and triaging large codebases for human reviewer attention. The production setup uses both in sequence.

03.03. What dataset construction approach works best for Solidity fine-tuning?

The strongest open results combine three sources: published audit reports paired with their findings as instruction-response examples, the SmartBugs Curated dataset for labeled examples across canonical bug classes, and synthetic instruction pairs generated from verified contracts on Etherscan. Per-example formatting follows the Alpaca instruction format with explicit code-block delimiters.