From Data Selection To Fine Tuning: The Technical Guide To Constructing LLM Models - Softcover

Aragon, Richard Anthony

Softcover

ISBN 13: 9798860315075

Publisher: Independently published, 2023

Synopsis

From Data Selection To Fine Tuning: The Technical Guide To Constructing LLM Models

Learn how to build and train large language models (LLMs) from scratch

Large language models (LLMs) are a type of artificial intelligence (AI) that are trained on massive datasets of text and code. This allows them to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

This book provides a technical guide to constructing LLM models. It covers everything from data selection to fine tuning, including:

• How to select the right data for your model
• How to preprocess your data
• How to choose the right architecture for your model
• How to train your model
• How to evaluate your model
• How to fine tune your model

The book also covers the ethical considerations involved in building and using LLM models.

If you are interested in learning how to build and train LLM models, then this book is for you. It is a comprehensive and up-to-date resource that will teach you everything you need to know.

Key Features:

• Covers all aspects of LLM model construction, from data selection to fine tuning
• Provides a hands-on approach with practical examples
• Discusses the ethical considerations involved in building and using LLM models
• Written by an expert in the field of AI

Audience:

This book is for anyone who wants to learn how to build and train LLM models. It is suitable for both beginners and experienced professionals.

Here are some additional points that you could include in the description:

• The book is written in a clear and concise style, making it easy to follow.
• The book is full of practical examples, so you can learn by doing.
• The book is up-to-date with the latest research in the field of LLMs.
• The book is written by an expert in the field of AI, so you can be confident that you are learning from the best.

Edition details

• Publisher: Independently published
• Publication date: 2023
• Language: English
• ISBN 13: 9798860315075
• Binding: Paperback
• Number of pages: 73