A Year 11 student developed an AI companion app called Soulor AI, utilizing a fine-tuned Qwen3 14B model. The development process highlighted several key challenges and solutions, including ensuring the model's output matched the required JSON format for the app's frontend, optimizing latency by moving to an NVIDIA H100 GPU, and implementing a gateway to manage cold starts for the scale-to-zero serverless endpoint. The student also identified and fixed two underlying bug patterns that caused seemingly unrelated outages. AI
IMPACT Demonstrates cost-effective fine-tuning of open-source LLMs for specialized applications, highlighting practical challenges in data formatting, latency, and deployment.
RANK_REASON The article details the process of fine-tuning an existing open-source model for a specific application, rather than a new model release from a frontier lab.
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