Large Language Models (LLMs) are revolutionizing coding, but their massive size comes at a cost – both computationally and environmentally. Imagine an LLM that's not only powerful but also eco-conscious. Researchers have tackled this challenge with a new pruning method called "Flab-Pruner," aiming to create leaner, greener code LLMs. Unlike traditional pruning techniques that focus on general language modeling, Flab-Pruner zeroes in on the specific needs of code generation. It strategically trims vocabulary, layers, and even individual neurons within the model, akin to a sculptor chiseling away at excess marble to reveal a masterpiece. This targeted approach reduces redundancy and streamlines the model without sacrificing performance. In fact, after a specialized "post-training" regimen, the pruned models often perform *better* than their bulkier counterparts. This research reveals that smaller, more efficient code LLMs are not just a possibility—they're a step towards a more sustainable future for AI-powered coding. The smaller size means less energy consumption, lower GPU usage, and a smaller carbon footprint. It also allows for deployment on less powerful hardware, democratizing access to advanced coding tools. While this research focuses on Python, it paves the way for greener LLMs across various programming languages, promising a future where coding is both powerful and environmentally responsible.