Fine-Tuning LLMs with PyTorch and Hugging Face: Train, Customize, and Deploy Large Language Models — A Hands-On Guide for Developers and AI Practitioners - Brossura
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Fine-Tuning LLMs with PyTorch and Hugging Face
Train, Customize, and Deploy Large Language Models — A Hands-On Guide for Developers and AI Practitioners
In the new era of open-weight AI, fine-tuning is no longer reserved for big tech. It’s the developer’s key to transforming powerful pretrained models into intelligent systems that understand your data, your tone, and your domain.
Fine-Tuning LLMs with PyTorch and Hugging Face is the definitive, hands-on guide for developers, engineers, and AI enthusiasts who want to move beyond prompt engineering and start teaching models to think. Through real-world examples, clean code, and practical workflows, this book takes you from your first training run to deploying a production-ready model that performs like it was built in-house.
You’ll learn how to:
- Set up your fine-tuning environment using PyTorch and the Hugging Face ecosystem
- Prepare, tokenize, and curate datasets that truly shape model behavior
- Run efficient fine-tuning using LoRA, QLoRA, and parameter-efficient methods
- Evaluate models for accuracy, coherence, and bias — quantitatively and qualitatively
- Deploy models with FastAPI, Gradio, and cloud or local infrastructure
- Apply fine-tuning to specialized domains like finance, healthcare, and law
- Compress and quantize models to run on low-memory devices without sacrificing quality
- Automate continuous learning pipelines and integrate retrieval systems (RAG) for real-world applications
What makes this book different is its developer-first focus. You’ll not only learn the how but the why behind each step — from understanding the transformer architecture to optimizing training loops for small GPUs. Each chapter reads like a real conversation between the model and the maker — bridging theory, experimentation, and production.
By the final chapters, you’ll see how fine-tuning reshapes your role from programmer to model designer. You’ll understand why the future of AI isn’t just about bigger models — it’s about smarter adaptation.
Whether you’re training your first conversational model, building a retrieval-augmented assistant, or deploying a fine-tuned LLaMA on your laptop, this book is your step-by-step roadmap to mastering the craft of model customization and deployment.
Perfect for:
Developers • AI engineers • Machine learning enthusiasts • Applied researchers • Tech founders exploring domain-specific AI
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Paperback. Condizione: new. Paperback. Fine-Tuning LLMs with PyTorch and Hugging FaceTrain, Customize, and Deploy Large Language Models - A Hands-On Guide for Developers and AI PractitionersIn the new era of open-weight AI, fine-tuning is no longer reserved for big tech. It's the developer's key to transforming powerful pretrained models into intelligent systems that understand your data, your tone, and your domain.Fine-Tuning LLMs with PyTorch and Hugging Face is the definitive, hands-on guide for developers, engineers, and AI enthusiasts who want to move beyond prompt engineering and start teaching models to think. Through real-world examples, clean code, and practical workflows, this book takes you from your first training run to deploying a production-ready model that performs like it was built in-house.You'll learn how to: Set up your fine-tuning environment using PyTorch and the Hugging Face ecosystemPrepare, tokenize, and curate datasets that truly shape model behaviorRun efficient fine-tuning using LoRA, QLoRA, and parameter-efficient methodsEvaluate models for accuracy, coherence, and bias - quantitatively and qualitativelyDeploy models with FastAPI, Gradio, and cloud or local infrastructureApply fine-tuning to specialized domains like finance, healthcare, and lawCompress and quantize models to run on low-memory devices without sacrificing qualityAutomate continuous learning pipelines and integrate retrieval systems (RAG) for real-world applicationsWhat makes this book different is its developer-first focus. You'll not only learn the how but the why behind each step - from understanding the transformer architecture to optimizing training loops for small GPUs. Each chapter reads like a real conversation between the model and the maker - bridging theory, experimentation, and production.By the final chapters, you'll see how fine-tuning reshapes your role from programmer to model designer. You'll understand why the future of AI isn't just about bigger models - it's about smarter adaptation.Whether you're training your first conversational model, building a retrieval-augmented assistant, or deploying a fine-tuned LLaMA on your laptop, this book is your step-by-step roadmap to mastering the craft of model customization and deployment.Perfect for: Developers - AI engineers - Machine learning enthusiasts - Applied researchers - Tech founders exploring domain-specific AI This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798273483422
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Paperback. Condizione: new. Paperback. Fine-Tuning LLMs with PyTorch and Hugging FaceTrain, Customize, and Deploy Large Language Models - A Hands-On Guide for Developers and AI PractitionersIn the new era of open-weight AI, fine-tuning is no longer reserved for big tech. It's the developer's key to transforming powerful pretrained models into intelligent systems that understand your data, your tone, and your domain.Fine-Tuning LLMs with PyTorch and Hugging Face is the definitive, hands-on guide for developers, engineers, and AI enthusiasts who want to move beyond prompt engineering and start teaching models to think. Through real-world examples, clean code, and practical workflows, this book takes you from your first training run to deploying a production-ready model that performs like it was built in-house.You'll learn how to: Set up your fine-tuning environment using PyTorch and the Hugging Face ecosystemPrepare, tokenize, and curate datasets that truly shape model behaviorRun efficient fine-tuning using LoRA, QLoRA, and parameter-efficient methodsEvaluate models for accuracy, coherence, and bias - quantitatively and qualitativelyDeploy models with FastAPI, Gradio, and cloud or local infrastructureApply fine-tuning to specialized domains like finance, healthcare, and lawCompress and quantize models to run on low-memory devices without sacrificing qualityAutomate continuous learning pipelines and integrate retrieval systems (RAG) for real-world applicationsWhat makes this book different is its developer-first focus. You'll not only learn the how but the why behind each step - from understanding the transformer architecture to optimizing training loops for small GPUs. Each chapter reads like a real conversation between the model and the maker - bridging theory, experimentation, and production.By the final chapters, you'll see how fine-tuning reshapes your role from programmer to model designer. You'll understand why the future of AI isn't just about bigger models - it's about smarter adaptation.Whether you're training your first conversational model, building a retrieval-augmented assistant, or deploying a fine-tuned LLaMA on your laptop, this book is your step-by-step roadmap to mastering the craft of model customization and deployment.Perfect for: Developers - AI engineers - Machine learning enthusiasts - Applied researchers - Tech founders exploring domain-specific AI This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798273483422
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