Outsourced model fine-tuning requires reliable task allocation, result validation, and incentives for quality. We present TrustCompute, a compute-sharing platform integrating two-stage adapter verification, reputation-aware scheduling, quality-based payments, and auditable ledger records for LoRA fine-tuning. Our evaluation combines synthetic workloads, real GPU-based fine-tuning, and a local Ethereum Virtual Machine (EVM) environment. Across 440 validation cases, all 400 invalid submissions were rejected and all 40 valid submissions were accepted. With 50% unreliable workers, reputation-aware scheduling achieved 100% task completion and reduced the mean failed-attempt time by 89% relative t... More >