Graphics Processing Units (GPUs) have excelled in parallelism and high throughput for dense, regular computations in modern computing. However, sparse computations, such as sparse matrix-matrix multiplication (SpMM), are essential for large-scale, data-intensive applications, where much of the data is inherently sparse. The challenge lies in the sparsity and irregularity of sparse matrices or tensors, which makes achieving high performance on GPU architectures difficult. Consequently, the utilization of suitable sparse data formats is imperative for achieving computational efficiency. Traditional computational libraries often require input in specific formats, which may not accommodate the diversity of matrix characteristics or the varying sparse patterns within a single matrix. While some frameworks support composable formats, they often lack guidance on how to compose these formats effectively or require costly auto-tuning for optimal performance. In this paper, we introduce LiteForm, a novel, lightweight framework designed to automatically compose sparse formats for SpMM computation. We start by presenting CELL, a composable format featuring a three-level blockwise representation that optimizes sparse data for GPUs. LiteForm uses this format and composes it based on the input's characteristics. First, it employs a lightweight model trained to predict whether the CELL format will yield good performance for a given sparse input matrix. Then LiteForm uses a low-overhead predictor and an SpMM cost model to automatically configure the format according to the characteristics of the input matrix. Our experimental evaluation indicates that LiteForm achieves a geometric mean speedup of 2.06×, 1.81×, 1.77×, and 4.18× in comparison to cuSPARSE, Sputnik, dgSPARSE, and TACO, respectively, and demonstrates speedups of 1.26× and 1.52× over state-of-the-art SparseTIR and STile, respectively.