Hassan El Mghari drops tev1-4B-experimental classifier

Fine-tuning lightweight open models for dedicated classification tasks is becoming so affordable that using large general-purpose LLMs for intent classification and routing is increasingly difficult to justify.

• –Extreme training cost efficiency: Spending only $17 to fine-tune a 4B parameter model highlights how accessible custom SLM training has become for independent builders.
• –High reproducibility: Open-sourcing the data recipe, weights, and step-by-step tutorial enables developers to easily adapt the approach to their own classification domains.
• –Viable production routing: Ultra-low serverless inference costs ($0.042/1M input tokens) make compact classifiers practical front-door filters and routers for high-volume pipelines.