Article’s

The proliferation of Large Language Models (LLMs) has significantly impacted software development, yet their substantial computational and resource demands create barriers to widespread accessibility. This paper details the development and evaluation of a Small Language Model (SLM) designed as an efficient, practical alternative for coding assistance. The primary goal is to create a lightweight, low-latency model specialized in Python, capable of performing real-time code completion and generating functions from natural language prompts. The methodology employs a transformer-based decoder-only architecture (100-300M parameters) trained on a filtered, high-quality dataset of open-source code. Model performance is assessed using the pass@k metric from the HumanEval benchmark for functional correctness, alongside measurements of inference speed and memory footprint to validate its efficiency. This research will deliver a proof-of-concept prototype, demonstrating that specialized SLMs can offer a sustainable and effective solution that enhances developer productivity while democratizing access to advanced AI-powered coding tools.