Voodoo dynamic quant goes MIT, and the method is simpler than anyone guessed.

The author kept it private for two months after claiming SOTA at aggressive quant levels on small Qwen3.5 GGUFs, then released the toolset at github.com/curvedinf/voodoo-dyn-quant. Run every quant level for every tensor at once, freeze the candidate quant weights from llama.cpp's ggml conversion, train a single scalar gate per tensor per quant level, with softmax keeping gradient flowing to all levels and an annealed tau forcing each tensor to settle on one choice. Gradient descent picks the per-tensor layout. He handed it over because he doesn't have time to scale it, which is the good reason to open source something.