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The LLM Toolkit: Fine-Tuning, Hyperparameter Tuning, and Building Hierarchical Classifiers

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Book Details
Language
English
Publishers
Independently published (16 May 2024)
Weight
0.06 KG
Publication Date
16/05/2024
Pages
33 pages
ISBN-13
9798325814099
Dimensions
15.24 x 0.2 x 22.86 cm
SKU
9798325814099
Author Name
Anand Vemula (Author)
Anand Vemula is a technology, business, ESG and Risk governance Evangelist. He has more than 27 plus years of experience. Has worked in MNC at a CXO level. Has been a part of various projects and forums across customers in BFSI, Healthcare, Retail, Manufacuring, Lifesciences, Energy Industry Verticals. Certified in all the technologies and Enterprise Digital ArchitectRead more about this authorRead less about this author
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In the age of artificial intelligence, large language models (LLMs) have become powerful tools for understanding and manipulating language. However, unlocking their full potential requires a deeper understanding of fine-tuning, hyperparameter optimization, and hierarchical classification techniques.

The LLM Toolkit equips you with a comprehensive guide to take your LLMs to the next level. This book delves into the concept of fine-tuning, explaining how to adapt pre-trained LLMs to specific tasks, such as text classification or question answering.

You'll explore various techniques for fine-tuning, including freezing and unfreezing layers, along with strategies for selecting and augmenting task-specific training data. Next, the book tackles the crucial topic of hyperparameter optimization.

LLMs have numerous parameters that can significantly impact their performance. This section guides you through the challenges of optimizing these hyperparameters, including the high computational cost and vast search space.

You'll discover common techniques like grid search, random search, and Bayesian optimization, along with their strengths and limitations. The book also explores the potential of using LLMs themselves to streamline hyperparameter optimization, paving the way for more efficient fine-tuning processes.

Finally, the book dives into hierarchical classification, a powerful approach for categorizing data with inherent hierarchical structures. You'll learn how to leverage LLMs to build hierarchical classifiers, exploring both multi-stage and tree-based approaches.

The book delves into the benefits of hierarchical classification for LLMs, including improved accuracy and better handling of ambiguous or noisy data. The LLM Toolkit is your one-stop shop for mastering these advanced LLM techniques.

Whether you're a researcher, developer, or simply interested in pushing the boundaries of language models, this book equips you with the practical knowledge and tools to unlock the full potential of LLMs and achieve cutting-edge results in your field. .

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