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

Vemula, Anand

  • 出版商: Independently Published
  • 出版日期: 2024-05-16
  • 售價: $590
  • 貴賓價: 9.5$561
  • 語言: 英文
  • 頁數: 34
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9798325814099
  • ISBN-13: 9798325814099
  • 相關分類: LangChain
  • 海外代購書籍(需單獨結帳)

商品描述

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.

商品描述(中文翻譯)

在人工智慧的時代,大型語言模型(LLMs)已成為理解和操作語言的強大工具。然而,要釋放它們的全部潛力,需要對微調、超參數優化和階層分類技術有更深入的了解。

《LLM工具包》為您提供了一本全面的指南,幫助您將LLMs提升到新的水平。本書深入探討微調的概念,解釋如何將預訓練的LLMs調整為特定任務,例如文本分類或問題回答。您將探索各種微調技術,包括凍結和解凍層,以及選擇和增強特定任務訓練數據的策略。

接下來,本書探討了超參數優化這一關鍵主題。LLMs擁有眾多參數,這些參數會顯著影響其性能。本節將指導您克服優化這些超參數的挑戰,包括高計算成本和廣泛的搜索空間。您將發現常見的技術,如網格搜索、隨機搜索和貝葉斯優化,以及它們的優勢和局限性。本書還探討了利用LLMs本身來簡化超參數優化的潛力,為更高效的微調過程鋪平道路。

最後,本書深入探討階層分類,這是一種強大的方法,用於對具有內在階層結構的數據進行分類。您將學習如何利用LLMs構建階層分類器,探索多階段和基於樹的兩種方法。本書深入探討了階層分類對LLMs的好處,包括提高準確性和更好地處理模糊或噪聲數據。

《LLM工具包》是您掌握這些先進LLM技術的一站式商店。無論您是研究人員、開發者,還是單純對推動語言模型的邊界感興趣,本書都為您提供了實用的知識和工具,以釋放LLMs的全部潛力,並在您的領域中實現尖端成果。