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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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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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improve the LLM's ability to pinpoint the most important data, enhancing its performance. Fine-tuning entails tailoring a LLM for a .
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This article explores recent research advancements in reasoning-optimized LLMs, with a particular focus on inference-time compute scaling.
When to Apply RAG vs Fine-Tuning - Medium
RAG systems often achieve better performance than fine-tuning while retaining more capabilities of the original LLM.
ultimate-guide-fine-tuning-llm_parthasarathy-2408.13296.md - GitHub
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Domain Mastery Book: Advanced Techniques for Fine-Tuning Large .
enhancing their performance, efficiency, and ethical implementation. inference while maintaining or even improving model performance.
The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs
10.3 Optimum: Enhancing LLM Deployment Efficiency . reduce its size and complexity, thereby enhancing its efficiency and performance.
Unlocking LLM Performance: Advanced Quantization Techniques .
Their primary goal is to improve memory use during inference, thereby accelerating the process. Because LLM inference is often memory-bound rather than .
Our paper on LLM cache generation at ICML24 - LinkedIn
novel approach to enhancing the efficiency of LLMs when handling extensive sequences. This method enhances LLM performance in .
[TMLR 2024] Efficient Large Language Models: A Survey - GitHub
Model-Centric Methods · Model Compression · Efficient Pre-Training · Efficient Fine-Tuning · Efficient Inference · Efficient Architecture .
LLM Optimization: How to Maximize LLM Performance - Deepchecks
technique when LLM customization is required for specific task usage. Fine-tuning can improve LLM efficiency in a specific domain, lower .
Methods for Improving Inference Speed During LLM Fine-Tuning
Could you suggest any ways to improve the response speed when using the results of fine-tuning an LLM model with Flower for inference tasks?
LLM Fine-Tuning: What It Is, Common Techniques, And More
Fine-tuning an LLM helps improve accuracy, efficiency, and the ability to perform very specific tasks by training the model on task-specific datasets.