LLM Fine Tuning with Open Models: Efficient Training, Adaptation, and Deployment with LoRA, QLoRA, and PEFT - Couverture souple

Ara, Husn

Couverture souple

ISBN 13 : 9798293007875

Editeur : Independently published, 2025

Synopsis

LLM Fine-Tuning with Open Models: Efficient Training, Adaptation, and Deployment with LoRA, QLoRA, and PEFT

Unlock the full potential of Large Language Models (LLMs) for your business, product, or research. This hands-on guide shows you how to fine-tune, align, and deploy powerful models like LLaMA, Mistral, Falcon, and more—without needing massive compute.
Whether you're building intelligent chatbots, document summarizers, domain-specific agents, or internal AI tools, this book walks you through practical techniques like LoRA, QLoRA, PEFT, RLHF, and DPO. Learn to serve your models using FastAPI, Hugging Face Hub, Streamlit, and LangChain—right from your laptop or a cloud GPU.
Inside you’ll learn how to:

• Prepare and fine-tune models on custom datasets
• Use parameter-efficient methods to cut costs and training time
• Evaluate, align, and optimize LLM outputs for safety and accuracy
• Build real-time apps, APIs, and agents powered by your fine-tuned model
• Deploy production-ready AI with ONNX, FSDP, and quantized models

Perfect for developers, ML engineers, startup teams, and tech leads looking to take control of Generative AI. No hype—just actionable techniques for training and deploying smarter, leaner, and safer AI systems.

Détails de l'édition

• Éditeur: Independently published
• Date d'édition: 2025
• Langue: anglais
• ISBN 13: 9798293007875
• Reliure: Broché
• Nombre de pages: 219
• Coordonnées du fabricant: Manufactured by Amazon on behalf of the author; https://www.amazon.fr/hz/contact-us; ; c/o Amazon Media EU S.à.r.l., 38 Avenue John F. Kennedy; Luxembourg; L-1855; Luxembourg