We describe a one-pass compression scheme which presumes no statistical properties of the data being compressed. The model structure adaptively selects a subset of first-order Markov contexts, based on an estimate of the candidate context's popularity. The probability distributions for the unselected (lumped) first-order contexts are made the same, reducing cost over a full first-order Markov model. Symbol repetitions are handled in special secondorder Markov contexts. The statistics for each symbol are adaptively determined by an extension of earlier work.