Fine-Tuning Small Open-Source LLMs to Outperform Large Closed-Source Models by 60% on Specialized Tasks

What this post added

This post details how Together AI's fine-tuning platform was used in conjunction with Parsed's evaluation expertise to fine-tune a small open-source model (Gemma 3 27B) to outperform larger proprietary models (like Claude Sonnet 4) by 60% on a specialized healthcare scribing task. It emphasizes the technical approach of using rigorous, domain-aligned evaluation frameworks to drive SFT and RL optimization, enabling significant cost reductions and performance improvements. The post also provides a comparative analysis of various models' performance before and after fine-tuning, illustrating the effectiveness of the platform for task-specific optimization.