Explore the potential of Large Language Models (LLMs) in advanced AI applications through this comprehensive conference talk. Gain insights into the fundamentals of LLMs, including transformer architecture, pre-trained parameters, and fine-tuning techniques. Learn practical ways to leverage LLMs through API integration, playgrounds, and model hosting platforms. Discover the concept of Retrieval-Augmented Generation (RAG) and its implementation steps to overcome limitations of standalone LLMs. Address cost concerns and explore reduction strategies for LLM usage. Understand how to improvise LLMs and apply them in various professional contexts, including software engineering and infringement detection. Benefit from real-world examples that demonstrate the practical applications of LLMs in advanced AI scenarios.
Overview
Syllabus
intro
preamble
agenda
understanding large language models
transformer architecture
pre-trained parameters
fine tuning
how can i leverage large language models llms?
using pre-trained models via api integration
using pre-trained models via playgrounds
hosting models - hugging face model hub
hosting models - aws sagemaker studio
deploy custom/fine tuned models
limitations of standalone llms
introducing retrieval-augmented generation rag
implementation steps for rag
cost concerns and reduction strategies for llms
cost reduction strategies
improvisation of llms
leveraging llms as a software engineer and tech professional
leveraging llm in infringement detection
real world examples
thank you
Taught by
Conf42
