For practical applications, the use of top-down query-driven proof-procedures is convenient for an efficient use and computation of answers using Logic Programs as knowledge bases. A 2-valued semantics for Normal Logic Programs (NLPs) allowing for top-down query-solving is thus highly desirable, but the Stable Models semantics (SM) does not allow it, for lack of the relevance property. To overcome this limitation we introduced in , and summarize here, a new 2-valued semantics for NLPs - the Layer Supported Models semantics - which conservatively extends the SM semantics, enjoys relevance and cumulativity, guarantees model existence, and respects the Well-Founded Model. In this paper we exhibit a space and time linearly complex transformation, TR, from one propositional NLP into another, whose Layer Supported Models are precisely the Stable Models of the transform, which can then be computed by extant Stable Model implementations, providing a tool for the immediate generalized use of the new semantics and its applications. TR can be used to answer queries but is also of theoretical interest, for it may be used to prove properties of programs.Moreover, TR can be employed in combination with the top-down query procedure of XSBProlog, and be applied just to the residual program corresponding to a query (in compliance with the relevance property of Layer Supported Models). The XSBXASP interface then allows the program transform to be sent to Smodels for 2-valued evaluation.