Exowatt Fine-Tunes Qwen3 Models for Alarm Triage and Energy-Market Analysis

Exowatt used Amazon SageMaker AI serverless model customization to fine-tune two Qwen3 models for alarm triage and energy-market analysis, with both outperforming the frontier-model baselines reported in an AWS Machine Learning Blog post. Qwen3-1.7B achieved 0.911 macro-F1 on the edge alarm-triage test, while Qwen3-14B achieved 0.975 macro-F1 on the cloud energy-market analysis test. Those results apply to the specific tasks tested. Exowatt’s reusable pipeline combines curated data, checked paraphrases, fine-tuning, and evaluation. It supports offline CPU deployment for the edge model and cloud inference for energy-market analysis. The post reported cloud inference costs of $0.60 per 1,000 answers, compared with $29.26 for Claude Opus 4.7.

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