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DTSTART:20260910T160000Z
DTEND:20260910T170000Z
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SUMMARY:Fine-Tuning LLMs - From Generic to Specialist: A Practical Walkthrough
DESCRIPTION:Generic LLMs are great generalists\, but they often fall short 
 on tone or structure when you need them for a specific task. In this 
 tutorial\, we'll look at how to fine-tune an LLM to a specific task through 
 additional training. We will discuss what fine-tuning actually does to a 
 model\, when it's better than just prompt engineering or other methods such 
 as retrieval-augmented generation (RAG)\, and how to approach it. We'll 
 walk through the full lifecycle\, from framing the problem and preparing 
 your data for fine-tuning\, to training\, evaluating\, and shipping a 
 specialized model. By the end\, you'll have a solid grasp of how 
 fine-tuning works and be ready to begin to apply it to your own tasks. Free 
 and open to faculty\, staff\, and students\, this session is led by a 
 K-State graduate student.\n\nThis session will not be recorded.\n\nBrought 
 to you by the K-State AI Consulting Service (KAICS).
LOCATION:Virtual
ORGANIZER;CN="Alice Anderson":MAILTO:aliceanderson@k-state.edu
CATEGORIES:AI Literacy
CONTACT;CN="Alice Anderson":MAILTO:aliceanderson@k-state.edu
STATUS:CONFIRMED
UID:LibCal-17356143
URL:https://cal.lib.k-state.edu/event/17356143
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