THRML provides tools for building sparse, heterogeneous probabilistic graphical models with efficient block sampling. The library compiles factor-based interactions to compact state representations, minimizing Python loops and maximizing array-level parallelism in JAX. It enables experimentation with block Gibbs sampling and discrete energy-based models, targeting GPU acceleration for probabilistic inference tasks.