CLI for building and testing DFlash-style speculative decoding draft models.

multiple tokens, and a verifier filters them using the main model’s confidence. Focuses on speed–accuracy tradeoffs, visualization, and modular design for easy benchmarking and research.

Simulates speculative decoding to find the optimal speculation length K across 576 configurations (3 draft models x 8 K values x 6 acceptance rates x 4 cost ratios). Key findings: 6.06x max speedup, breakeven at cost_ratio=0.25, optimal K grows from 1-3 at 50% acceptance to 7-15 at 95% acceptance.