Fine-Tuning Qwen3-8B with Tinker: From 50% to 76% Accuracy, and Burning Through My Budget

Developer Gramli shares their experience participating in the Hacktoberfest Open-Source AI Challenge by building 'Complexity Scorer,' a tool designed to optimize AI API costs. By fine-tuning the Qwen3-8B model using the Tinker platform, the project aims to classify prompt complexity, allowing applications to route requests to more cost-effective models. The author details the technical journey, including the selection of a hybrid model architecture and the challenges of dataset generation. Notably, an accidental misconfiguration led to an unexpected spike in training costs, highlighting the risks of automated agent workflows. Despite the hurdles, the final model achieved a 76% accuracy rate in classifying prompt complexity. The project is open-source, with the fine-tuned adapter available on Hugging Face, encouraging developers to experiment with local inference and reduce dependency on expensive, closed-source AI providers.