7B模型性能媲美72B，提升本地生活服务AI部署效率
LocalGPT: Benchmarking and Advancing Large Language Models for Local Life Services in Meituan
- 用7B小模型通过微调和智能流程优化，实现高性价比推理
- 7B模型在任务表现上接近72B大模型，降低部署成本
- 适合需要高效、低成本AI服务的本地生活平台使用
大型语言模型（LLMs）在多个领域展现出卓越能力，近年来得到广泛应用。本文聚焦本地生活服务场景，构建了全面的评估基准，系统评测多种LLMs在多样化任务中的表现。为提升模型效能，研究探索了模型微调与基于代理的工作流两种策略。结果表明，即使较小的7B模型也能达到远大于其规模的72B模型的性能水平，有效平衡推理开销与模型能力。该优化显著提升了LLMs在真实在线服务中部署的可行性与效率，使其更适用于本地生活类应用。
原文摘要 · Abstract (English)
Large language models (LLMs) have exhibited remarkable capabilities and achieved significant breakthroughs across various domains, leading to their widespread adoption in recent years. Building on this progress, we investigate their potential in the realm of local life services. In this study, we establish a comprehensive benchmark and systematically evaluate the performance of diverse LLMs across a wide range of tasks relevant to local life services. To further enhance their effectiveness, we explore two key approaches: model fine-tuning and agent-based workflows. Our findings reveal that even a relatively compact 7B model can attain performance levels comparable to a much larger 72B model, effectively balancing inference cost and model capability. This optimization greatly enhances the feasibility and efficiency of deploying LLMs in real-world online services, making them more practical and accessible for local life applications.
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