A compact, experimentally-minded model built around a fundamentally different architecture than the transformer mainstream. Mamba uses selective state spaces instead of attention, which means it handles sequences with fixed memory regardless of length — a notable contrast to how most models work. At 130M parameters, it's quite small and raw, better suited for research exploration than polished task completion.