Large language models are powerful because of their state-of-the-art language processing abilities. But, they come at the cost of being extremely resource-intensive, and are steadily growing in size. As a result, compressing such models for resource- constrained devices is an active and promising re- search area. In spite of their current popular- ity, many novel compression techniques lack im- plementation for GPT models. We apply the XTC pipeline, consisting of layer-reduction and quantisation through knowledge distillation, to a CodeGPT generative model. The resulting mod- els are evaluated on the CodeXGLUE line-level code-completion benchmark. Based on this, we demonstrate that (1) XTC can be adapted to GPT- like models, translating many of the findings of the original study; (2) a 6-layer reduction with 1-bit weight and 8-bit activation quantisation is able to reduce model size 15×, in addition to almost dou- bling inference speed, with minimal performance degradation. The resulting compressed models show promise for use in local code generation. By showing that a novel compression technique can be adapted to GPT-like models, we hope to further in- spire research in this field.