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README.md
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# Summary
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Solidity LLM, despite its compact 2B parameter size, delivers standout performance in generating Solidity smart contracts. It achieved the highest compilation success rate (83%), showcasing robust syntactic and structural understanding. Its strong OpenZeppelin compliance (65%), though slightly behind very large models like GPT-4.5, is impressive given the scale difference, reflecting reliable use of industry-standard patterns and libraries.
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Further, Solidity LLM ranked highest in gas efficiency (72%), producing optimized code suitable for cost-sensitive deployments. While the security score (58%) indicates room for improvement, the model consistently generated secure-enough contracts for practical use. Its concise output (70% LOC score) also suggests an efficient coding style, balancing brevity with completeness.
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# Summary
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Solidity LLM, despite its compact 2B parameter size, delivers standout performance in generating Solidity smart contracts. It achieved the highest compilation success rate (83%), showcasing robust syntactic and structural understanding. Its strong OpenZeppelin compliance (65%), though slightly behind very large models like GPT-4.5, is impressive given the scale difference, reflecting reliable use of industry-standard patterns and libraries.
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Further, Solidity LLM ranked highest in gas efficiency (72%), producing optimized code suitable for cost-sensitive deployments. While the security score (58%) indicates room for improvement, the model consistently generated secure-enough contracts for practical use. Its concise output (70% LOC score) also suggests an efficient coding style, balancing brevity with completeness.
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Overall, Solidity LLM proves to be a resource-efficient, reliable, and well-balanced model for Solidity code generation.
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Looking ahead, future releases will focus on improving support for newer versions of the Solidity language and OpenZeppelin libraries, enhancing user interaction by enabling contract modifications, expanding compatibility to other languages like Rust, and developing larger models capable of handling longer context windows.
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