DictLLM shrinks a 7B model 5.1× with shared dictionaries and sparse coefficients, adding 2.9 points of perplexity
On mobile devices the binding constraint for large language models is weight memory, not computation. DictLLM splits weight matrices within grouped layers into one shared dictionary plus layer-specific sparse coefficients, all computed from a pretrained model without retraining. On LLaMA-7B it compresses the model 5.1× with only a 2.9-point rise in WikiText-2 perplexity, a compression ratio about 3.6× that of the state-of-the-art low-rank method.