The fast development of exoplanetary observation and transit spectroscopy enables the effective study of exoplanetary atmospheres. The atmospheric features can be obscured in spectra with the formation of clouds and hazes. It adds difficulty to observing the atmospheric chemistry and imposes uncertainties in constraining the atmospheric parameters using transit spectroscopy. The high-quality transit spectra offered by the next generation of facilities, such as JWST and Ariel, provide the possibility to observe the signatures of clouds and hazes via transit spectroscopy, thus allowing the detailed study of their size distribution. In this work, we built YunMa, an exoplanetary cloud retrieval model, to deepen the understanding of cloud formation from transit spectra and cloud microphysics, which is an essential mechanism of cloud formation. In YunMa, we simulated the vertical cloud particle size distribution and the corresponding transit spectra of the cloudy atmosphere. We took advantage of the TauREx 3 platform, an open Bayesian framework for spectral retrieval, to enable the retrieval function of YunMa. We validated our model by comparing our simulation with previous literature results. We set the sedimentation efficiency (fsed), which controls the cloud microphysics, as a free parameter in retrieval. We evaluate the retrieval performances on a synthetic temperate super-Earth. We aim to present YunMa as a demonstration of retrievals with parameterised cloud microphysics considered.