Qwengram-0.8B grafts a 51B-parameter n-gram memory onto a 0.8B backbone for 5.05% lower perplexity.

The experiment froze both the Qwen3.5-0.8B backbone and Qwen3.8-Flash-Next's PLE memory, training only a small R=1 reader at decoder layers 3 and 9 with a token-level gate, on 15M tokens using free Kaggle GPUs. Validation perplexity fell from 18.28 to 17.35, and the real memory beat both random and permuted controls. A 20M-token reader regressed on math and strong fixed injection hurt LAMBADA, so there's a narrow window. GGUFs and a llama.cpp path are public.