Fine-Tuning LLM: Advanced Techniques for Optimizing AI Performance with PyTorch and Hugging Face

Jones, Camila

ISBN 13: 9798311657945

Verlag: Independently published, 2025

Beschreibung

Bestandsnummer des Verkäufers 50033893-n

Bibliografische Details

• Titel: Fine-Tuning LLM: Advanced Techniques for Optimizing AI Performance with PyTorch and Hugging Face
• Verlag: Independently published
• Erscheinungsjahr: 2025
• Sprache: Englisch
• ISBN-13: 9798311657945
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

"Fine-Tuning LLMs: Advanced Techniques for Optimizing AI Performance with PyTorch and Hugging Face" is a definitive guide for practitioners, researchers, and developers who seek to harness the full potential of large language models in real-world applications. This comprehensive book demystifies the fine-tuning process, offering a step-by-step roadmap from foundational principles to state-of-the-art techniques.

Inside, you’ll discover how to leverage the powerful ecosystems of PyTorch and Hugging Face to tailor pre-trained models for specialized tasks across diverse industries—be it legal, medical, or financial. Learn how to prepare and preprocess data, implement parameter-efficient strategies, and optimize hyperparameters to ensure your models are both robust and scalable. In addition, the book covers essential topics such as ethical considerations, fairness, security, and responsible AI practices, equipping you with the tools needed to deploy safe and reliable systems.

Packed with detailed case studies, real-world examples, and practical code snippets, this book not only teaches you the technical nuances of fine-tuning but also guides you through deployment, integration, and continuous learning in production environments. Whether you’re a seasoned AI professional or an enthusiastic newcomer, "Fine-Tuning LLMs" provides the insights and methodologies necessary to transform cutting-edge research into impactful, high-performance applications.

Embrace the future of AI with this essential resource that combines deep technical expertise with practical, hands-on guidance for optimizing language models to achieve unparalleled performance and efficiency.