Researchers Break 1.58-Bit Barrier for Ternary LLMs

A technical report describes a method that surpasses the previously considered 1.58-bit-per-weight limit for ternary (weights in {-1, 0, 1}) large language models. The approach reportedly achieves lower perplexity and better task performance than standard ternary models at the same model size. This could enable more efficient deployment of LLMs on resource-constrained devices.