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Fine-Tuning is Broken: Why You're Doing It Wrong // Tanmay Chopra // AI in Production 2025

Posted Mar 24, 2025 | Views 5
# Fine-tuning LLMs
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Tanmay Chopra
Founder / CEO @ Emissary

Tanmay is a machine learning engineer at Neeva, where he's currently engaged in reimagining the search experience through AI - wrangling with LLMs and building cold-start recommendation systems. Previously, Tanmay worked on TikTok's Global Trust&Safety Algorithms team - spearheading the development of AI technologies to counter violent extremism and graphic violence on the platform across 160+ countries. Tanmay has a bachelor's and master's in Computer Science from Columbia University, with a specialization in machine learning.

Tanmay is deeply passionate about communicating science and technology to those outside its realm. He's previously written about LLMs for TechCrunch, held workshops across India on the art of science communication for high school and college students, and is the author of Black Holes, Big Bang and a Load of Salt - a labor of love that elucidated the oft-overlooked contributions of Indian scientists to modern science and helped everyday people understand some of the most complex scientific developments of the past century without breaking into a sweat!

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SUMMARY

Fine-tuning isn't just about throwing more data at a model with the same pretraining loss. That’s just extended pretraining. True fine-tuning means modifying loss functions, adjusting output heads, and optimizing for real-world constraints like confidence calibration, consistency, and latency. This talk explores how misguided fine-tuning practices lead to brittle, inefficient models and demonstrates practical strategies to tailor models to production needs. We dive into when to finetune, the advantages of true finetuning (from output constraints to confidence scores to drastically lower latency) and show how finetuning can be about more than just style.

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TRANSCRIPT

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