How to Train a Small Language Model: The Complete Guide for 2026

Small language models (SLMs), defined as having fewer than 14 billion parameters, offer cost-effective solutions for enterprise AI by allowing data to remain on local servers, reducing reliance on expensive API calls. The document outlines three paths to train SLMs: building from scratch, fine-tuning, and knowledge distillation, each with varying costs, timelines, and skill requirements. Fine-tuning is the most common approach, often using techniques like LoRA to adapt existing models for domain-specific tasks at reduced costs and timescales. The guide emphasizes the importance of high-quality, domain-relevant datasets to optimize model performance, with a reminder that proper evaluation is crucial to avoid deployment issues. It also discusses deployment strategies and the importance of continuous learning to counteract model drift, suggesting that while SLMs excel in specific, focused tasks with privacy benefits, large language models (LLMs) are better suited for more complex, broad-domain applications.