Developer fine-tunes a custom NER model using Gemini for just $9

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A developer shared a project in which they used Google's Gemini to help train a named entity recognition (NER) model designed to eventually replace it for that specific task. The process involved using Gemini to generate or label training data, which was then used to fine-tune a smaller, cheaper model. The total cost of the experiment came to just $9, highlighting the cost efficiency of using large language models to bootstrap smaller specialized ones. The project was shared on Hacker News, where it garnered modest attention with 11 points and a small number of comments.

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Two devs built an offline YOLO26 posture detector in 7 days — here's what broke

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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