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BoltNet — The Journey to Sub-200ms Voice AI

Phase 0 — Architecture

Designed BoltNet: unified ternary transformer. One model that listens, thinks, and speaks. Weights are {-α, 0, +α} per channel — addition-only inference, no floating point multiplies.

Phase 1 — STT Distillation

Whisper Small (244M params) distilled into BoltNet encoder (4 layers, 384-dim). TTQ learns optimal ternary thresholds. ~33% weight sparsity = free pruning.

Phase 2 — LLM Distillation

Mistral 7B reasoning compressed into 6-layer ternary core. Trained on 20 British sales conversation patterns. Per-channel scaling preserves conversational nuance.

Phase 3 — TTS Distillation

British female voice (Charlotte) cloned into ternary decoder. 4-layer mel generator produces natural speech from token embeddings without a separate TTS model.

Phase 4 — End-to-End Finetuning

Joint training on conversational audio pairs. Audio embeddings flow directly through reasoning into speech generation — skipping the text bottleneck entirely.

Phase 5 — GPTQ Ternary Refinement

Hessian-optimal weight assignment. Second-order information decides which weights become -1, 0, or +1. Final model: ~50MB, <200ms on CPU, zero API costs. Open-source Bland.ai.

            
                View the full journey on GitHub
            
             |  Architecture: TTQ + GPTQ + Triple Distillation  |  Training...

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