0.18.0

Quantized-gradient histograms are now the default (quantize_gradients=True): the split search runs on ~15-bit quantized gradient/hessian pairs packed one-integer-per-sample into integer histograms (the LightGBM-4 quantized-training idea, adapted). Summed single-model fit time fell 26% on the Grinsztajn suite and 20% on the high-cardinality suite (bagged Ens8 arm likewise -19%) at benchmark-flat accuracy; leaf values are always computed from the unquantized float gradients. Deterministic per random_state; quantize_gradients=False restores exact float64 histograms. Also: tree growth now runs one fused kernel launch per level (bit-identical; biggest on small fits). Full changelog in CHANGELOG.md.