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Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques by Peyman Passban, Andy Way, Mehdi Rezagholizadeh
- Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques
- Peyman Passban, Andy Way, Mehdi Rezagholizadeh
- Page: 183
- Format: pdf, ePub, mobi, fb2
- ISBN: 9783031857461
- Publisher: Springer Nature Switzerland
Download Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques
Download ebook free for pc Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques by Peyman Passban, Andy Way, Mehdi Rezagholizadeh
This book is a pioneering exploration of the state-of-the-art techniques that drive large language models (LLMs) toward greater efficiency and scalability. Edited by three distinguished experts—Peyman Passban, Mehdi Rezagholizadeh, and Andy Way—this book presents practical solutions to the growing challenges of training and deploying these massive models. With their combined experience across academia, research, and industry, the authors provide insights into the tools and strategies required to improve LLM performance while reducing computational demands. This book is more than just a technical guide; it bridges the gap between research and real-world applications. Each chapter presents cutting-edge advancements in inference optimization, model architecture, and fine-tuning techniques, all designed to enhance the usability of LLMs in diverse sectors. Readers will find extensive discussions on the practical aspects of implementing and deploying LLMs in real-world scenarios. The book serves as a comprehensive resource for researchers and industry professionals, offering a balanced blend of in-depth technical insights and practical, hands-on guidance. It is a go-to reference book for students, researchers in computer science and relevant sub-branches, including machine learning, computational linguistics, and more.
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This book is a pioneering exploration of the state-of-the-art techniques that drive large language models (LLMs) toward greater efficiency and scalability.
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efficiency of LLM/VLM fine-tuning and inference across three key directions. fine-tuning with superior performance. Second, we explore .
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Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques. Peyman Passban Edited by Andy Way , Mehdi Rezagholizadeh.
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The results showcase the capability of fine-tuned models to surpass the accuracy of zero-shot LLMs, providing superior question and answering capabilities.
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Inference Techniques (Machine Translation: Technologies and Applications, 7, Band 7). PRICES MAY VARY. This book is a pioneering exploration of the state-of .
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