manifest.json

{
  "entry_class": "model.BitSerialReducer",
  "output_base": 2,
  "framework": "pytorch",
  "model_description": "Compute-optimized NeuralHorner v8 inference wrapper using the original ~471K-parameter bidirectional two-layer GRU transition. Stores weights in bfloat16, thresholds logits directly, reuses static feature channels, and removes one redundant modular-reduction pass by streaming one original operand directly through the learned Horner multiply transition.",
  "training_description": "Uses the published TrickyRex/bitserial-modmul-v8 weights (MIT), warm-started and fine-tuned by its author on one-step modular transitions. This derivative changes checkpoint precision and the mathematically equivalent inference schedule; it does not retrain the learned transition."
}