Hands-On Small Language Models (Early Release) | 1 MB
Title: Hands-on Small Language Models (for Duc Ka)
Author: Alexander Thomas
Category: Nonfiction, Computers, Advanced Computing, Programming, Data Modeling & Design, Computer Science, General Computing
Language: English | 308 Pages | ISBN: 9781098150921
Description:
Large language models have reshaped what’s possible in AI-but their size, cost, and complexity can make them difficult to use in real-world production. Small language models (SLMs) offer a more practical alternative: they’re efficient, focused, and built for applications that demand agility, privacy, and control. Hands-On Small Language Models provides a hands-on guide to understanding, building, and deploying these compact models to power specialized agentic applications. Author Alex Thomas, principal data scientist at John Snow Labs, draws on years of experience in natural language processing and applied AI to show how SLMs are democratizing the generative AI landscape. Through clear explanations and guided projects, including the development of a multi-functional movie chatbot, you’ll learn how to combine, deploy, and monitor SLMs both locally and in the cloud.
Large language models have reshaped what’s possible in AI-but their size, cost, and complexity can make them difficult to use in real-world production. Small language models (SLMs) offer a more practical alternative: they’re efficient, focused, and built for applications that demand agility, privacy, and control. Hands-On Small Language Models provides a hands-on guide to understanding, building, and deploying these compact models to power specialized agentic applications. Author Alex Thomas, principal data scientist at John Snow Labs, draws on years of experience in natural language processing and applied AI to show how SLMs are democratizing the generative AI landscape. Through clear explanations and guided projects, including the development of a multi-functional movie chatbot, you’ll learn how to combine, deploy, and monitor SLMs both locally and in the cloud.
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