Overview

Efficient Fine-Tuning of LLMs (First Edition) Practical Techniques Using LoRA, QLoRA, and PEFT for Scalable and Cost-Effective NLP Model Deployment Unlock the power of Large Language Models (LLMs) without the need for expensive infrastructure or complex workflows. Efficient Fine-Tuning of LLMs is a hands-on guide designed for developers, data scientists, entrepreneurs, and AI enthusiasts who want practical, actionable methods for adapting LLMs to real-world tasks. This book introduces parameter-efficient fine-tuning (PEFT) techniques such as LoRA and QLoRA, showing you how to reduce resource consumption while maintaining strong model performance. Through clear explanations and step-by-step implementations, you'll learn how to fine-tune open-source models on consumer-grade hardware and deploy them effectively for applications like chatbots, text summarization, and customer support systems. Inside, you will learn how to: Prepare and clean datasets for domain-specific tasks. Implement LoRA and QLoRA with Hugging Face tools. Optimize training with gradient checkpointing, mixed precision, and distributed strategies. Deploy fine-tuned models using cost-effective solutions such as Gradio, FastAPI, and cloud platforms. Ensure fairness, reliability, and safe use of LLMs in production environments. Whether you are building your first NLP model or seeking efficient ways to bring AI into your business, this book provides the knowledge and workflows to get started quickly. Efficient Fine-Tuning of LLMs (First Edition) is your essential resource for making LLMs practical, affordable, and production-ready.

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