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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

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Read online free books no download Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques (English literature) PDF iBook RTF 9783031857461

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.

Scaling LLM Test-Time Compute Optimally Can be More Effective .
improvement from existing fine-tuning techniques. They left the exploration . Our test-time compute techniques instead improve performance by up to 30% in some .
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
We develop SIFT, an effective data selection method for fine-tuning LLMs. We show that test-time fine-tuning with SIFT can significantly and robustly improve .
Enhancing LLM Performance, eBook by Peyman Passban - Booktopia
Each chapter presents cutting-edge advancements in inference optimization, model architecture, and fine-tuning techniques, all designed to .
Enhancing Llm Performance: Efficacy, Fine-tuning, And Inference .
Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques. Peyman Passban Edited by Andy Way , Mehdi Rezagholizadeh.
Top Tools and Techniques for LLM Fine-Tuning: A Comprehensive .
enhancing their performance significantly. As . fine-tuning process, enhancing the overall efficiency and effectiveness of the model.
Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference .
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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