Our main approach involves performing domain adaptation of an LLM for generating text to code. We selected the model CodeGen-350M-mono , an autoregressive transformer model with next-token generation capabilities to use as our baseline, which was pretrained for text-to-code tasks on a general programming-related corpus curated from GitHub and was further fine-tuned for Python programming. We highlight the modest size of the model -350M is the smallest codegen model that gets sensible answers on Python code benchmarks. This aligns with recent trends in responsible AIusing smaller models to save computing resources and reduce CO 2 emissions . We understand that bigger models might be more capable . However, the approach outlined in this paper may become more accessible to a broader audience as smaller models are released, which are not only less expensive to finetune but also more likely to be applicable on edge devices, e.i., on-device language models .