Although schematic-level optimization methods have been well studied in the literature, limited to layout generation and parasitic extraction, few tools can provide reliable post-layout level optimization of analog/RF post-layout. In fact, in order to quickly identify feasible regions and avoid unnecessary simulations during sizing optimization process, the surrogate model which can predict post-layout simulation performance metrics must be built. However, building a accurate model is yet a challenging task because of the conflict between sufficient samples and expensive simulation costs. Fortunately, schematic simulation costs are more cheaper than post-layout simulation costs, and certain similarities of circuit behaviors exist in between the schematic and post-layout simulation stage. Motivated by these observations, we propose a Fine-tuning based Model Fusion (FMF) technique to reuse some knowledge from early data. We first copy a pre-trained Artificial Neural Networks (ANN) model parameters which is carefully trained by abundant schematic-level samples, and fine-tune this model by few post-layout samples. Then, the number of post-layout samples required for training can be greatly reduced. Finally, we conduct experiments on three different analog/RF circuits and verify the efficiency of FMF. FMF can achieve a more than 9x sample reduction and highest modeling accuracy over traditional modeling techniques.