I Trained a Small Language Model from Scratch (nwosunneoma.medium.com)

An engineer built and trained a small language model (SLM) from scratch to show a pragmatic alternative to massive LLMs: a 16-million-parameter transformer (6 layers, 6 heads, 384-dim embeddings, 50,257-token vocab, 128-token context) trained on automotive customer-service call transcripts via a BYOD (bring your own data) pipeline. Using a public Hugging Face dataset, the pipeline preserved transcript metadata and speaker IDs, and training loss fell from 9.2 to 2.2, demonstrating the model learned domain-specific conversation structure and terminology. The model’s footprint (~64 MB in 32-bit), fast inference, and rapid fine-tuning make it suitable for edge or embedded deployments and real-time customer-support use cases. This work is significant because it reframes AI ROI: when 42% of AI projects deliver zero ROI and 88% of POCs never reach production, smaller specialized models offer predictable economics, easier integration, data privacy, and consistent task-level performance versus costly, general-purpose LLMs. Key implications include strict data-quality needs (remove metadata artifacts, normalize speakers), trade-offs in generality (SLMs won’t handle unrelated tasks), and operational considerations (multiple SLMs may be deployed with standardized pipelines, centralized monitoring, and automated data management). The takeaway: targeted SLMs can deliver measurable business value faster and cheaper than scaling to ever-larger, expensive models.