Tensor Train Language Models (TTLMs) offer significant memory savings by representing text sequences as tensor networks, but naive implementations struggle with long-range dependencies and limited flexibility. We introduce a modular TTLM framework that combine local and non-local context modules to achieve scalable language modeling. Our non-local modules, inspired by entanglement in quantum information theory, enable efficient modeling of long-range interactions between hidden states. Experiments on Penn Treebank and Wikitext datasets show that our modular TTLM, including entanglement-augmented variants, outperform naive baselines. These results highlight TTLMs as a promising, memory-efficient alternatives for modern language modeling.