FairTune Market: A Fair and Trustworthy Marketplace for Fine-Tuned LLMs via Posted-Price & Proper-Scoring Mechanisms

Personalized fine-tuning of Large Language Models (LLMs) enhances user experience but incurs substantial computational costs. Recent studies demonstrate that initializing training from style-similar fine-tuned models significantly reduces adaptation overhead, turning these models into reusable, tradable assets. However, emerging model marketplaces suffer from inherent information asymmetry: providers may misrepresent model capabilities, while model buyers cannot verify utility prior to purchase, leading to adverse selection and potential market collapse. We introduce FairTune Market to mitigate these risks. Our framework combines a posted-price mechanism with a truncated proper scoring rule, conditioning payments on performance under text-generation verification. This design incentivizes truthful reporting as an equilibrium strategy while bounding downside risk from stochastic evaluation. Compared with strong market baselines, FairTune helps buyers find better-matched starting checkpoints, reducing total downstream fine-tuning cost by 72.4\% while recovering 96.3\% of the welfare of an Oracle market with perfect style information. It also improves market health: provider misreporting drops by 96.5\% (from 0.424 to 0.015), and all providers remain profitable and willing to participate in the default setting. These gains are robust across market scales, evaluation noise levels, and niche-provider scenarios. A semi-real reuse experiment on sentence-transformer embeddings from 10 text domains further shows that FairTune achieves near-Oracle matching in a realistic style-embedding space.