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

LLM Fine Tuning with Open Models (Paperback)

Paperback. Condición: new. Paperback. 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 datasetsUse parameter-efficient methods to cut costs and training timeEvaluate, align, and optimize LLM outputs for safety and accuracyBuild real-time apps, APIs, and agents powered by your fine-tuned modelDeploy production-ready AI with ONNX, FSDP, and quantized modelsPerfect 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798293007875

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