Sub-1-Bit LLM Compression via Latent Factorization

A technical paper introduces a method for compressing large language models to sub-1-bit precision using latent factorization, presented as research. The approach aims to reduce model size while maintaining performance, targeting efficiency gains for deployment. Details on the factorization technique and benchmark results are not specified in the source.