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UID:pretalx-2024-BZGQMC@cfp.scipy.org
DTSTART;TZID=PST:20240709T133000
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DESCRIPTION:Generative AI systems built upon large language models (LLMs) h
 ave shown great promise as tools that enable people to access information 
 through natural conversation. Scientists can benefit from the breakthrough
 s these systems enable to create advanced tools that will help accelerate 
 their research outcomes. This tutorial will cover: (1) the basics of langu
 age models\, (2) setting up the environment for using open source LLMs wit
 hout the use of expensive compute resources needed for training or fine-tu
 ning\, (3) learning a technique like Retrieval-Augmented Generation (RAG) 
 to optimize output of LLM\, and (4) build a “production-ready” app to 
 demonstrate how researchers could turn disparate knowledge bases into spec
 ial purpose AI-powered tools. The right audience for our tutorial is scien
 tists and research engineers who want to use LLMs for their work.
DTSTAMP:20260417T064306Z
LOCATION:Ballroom D
SUMMARY:Generative AI Copilot for Scientific Software – a RAG-Based Appro
 ach using OLMo - Don Setiawan\, Anshul Tambay\, Cordero Core\, Niki Burggr
 af\, Anant Mittal\, Ishika Khandelwal\, Anuj Sinha\, Madhav Kashyap\, Vani
  Mandava
URL:https://cfp.scipy.org/2024/talk/BZGQMC/
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