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Ten Commandments for Deploying Fine-Tuned Models

Kyle Corbitt outlines ten principles for deploying fine-tuned models in production, advising teams to prioritise prompting, data quality, model selection, and evaluation before fine-tuning.

May 25 · · primary fetch1 sourceupdated May 25 ·

Gemini-in-Google-Slides is highlighted as a useful tool for summarizing presentations. Kyle Corbitt's talk on deploying fine-tuned models in production emphasizes avoiding fine-tuning unless necessary, focusing on prompting, data quality, appropriate model choice, and thorough evaluation. Anthropic showcased feature alteration in Claude AI, demonstrating control over model behavior and increased understanding of large language models.

Open-source models like GPT-4o are approaching closed-source performance on benchmarks like MMLU for simple tasks, though advanced models remain necessary for complex automation.

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