LoRA vs Gemini, to Build AI & LLM/RAG Apps

LoRA vs Gemini, to Build AI & LLM/RAG Apps

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With case study: marketing chatbot. This hands-on workshop is for developers and AI professionals, featuring state-of-the-art technology. Recording and GitHub material will be available to registrants who cannot attend the free 60-min session.

Overview

Come join our upcoming webinar on the transformative power of AI in building personalized marketing chatbots!?

This session will delve into how AI, particularly Google's Gemini and LoRA, is revolutionizing the way we approach customer engagement, making it possible to tailor interactions in real-time to each user’s unique preferences. With AI-driven personalization at the forefront, businesses can significantly improve customer satisfaction, increase sales, and foster loyalty. Expect to witness a live demo and code-share during the webinar, showcasing the practical applications of these technologies.

In this session, we will cover various techniques centered around Large Language Models (LLMs), such as methods to build personalized chatbots, secure LLM-based apps for specialized domains like healthcare, and full-stack AI apps with front-end clouds such as Vercel Next.js paired with powerful back ends like SingleStore.

You’ll learn:

  • The fundamentals of AI-powered personalized marketing chatbots.
  • How Gemini and LoRA can be leveraged to improve chatbot accuracy and personalization.
  • A live demonstration of creating a personalized marketing chatbot using AI technologies.
  • Techniques for domain adaptation and personalization of LLMs, including prompting and fine-tuning.
  • How to use parameter-efficient fine-tuning methods like LoRA to adapt LLMs to specific tasks with minimal resources.

Speaker:

Vinija Jain, Machine Learning at Amazon

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Vincent Granville

Chief AI Scientist, GenAItechLab.com

4 个月

LoRA stands for Low-Rank Adaptation. It is a technique used to fine-tune LLMs in a parameter efficient way. This doesn't involve fine-tuning whole of the base model, which can be huge and cost a lot of time and money. It is similar to the strategy used in my xLLM, consisting of hundreds of specialized sub-LLMs: you can fine-tune hyperparameters on just one (or a few) sub-LLM, at least as a starting point.

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