The LLM Engineer's Toolkit: Build, Train, and Deploy GPT-Style Language Models from Scratch Using Python, Transformers and PyTorch - Softcover

Darian, Juno

9798288293931: The LLM Engineer's Toolkit: Build, Train, and Deploy GPT-Style Language Models from Scratch Using Python, Transformers and PyTorch

Softcover

ISBN 13: 9798288293931

Verlag: Independently published, 2025

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The LLM Engineer’s Toolkit: Build, Train and Deploy GPT-style Language Models from Scratch Using Python, Transformers and PyTorch

Unlock the Power of GPT: Master Language Models from the Ground Up
Whether you're an AI researcher, ML engineer, data scientist, or curious developer—The LLM Engineer’s Toolkit is your ultimate guide to building and deploying Large Language Models (LLMs) like GPT from scratch using Python, PyTorch, and Hugging Face Transformers.
This hands-on, no-fluff manual gives you everything you need to design, train, fine-tune, and serve powerful GPT-style models for real-world use.

What You’ll Learn:
• The fundamentals of Transformer architectures and tokenization techniques
• Pretraining strategies, dataset engineering, and scaling LLMs across GPUs
• Instruction tuning, LoRA, QLoRA, and other fine-tuning methods for practical deployment
• Real-world optimization: quantization, inference acceleration, and containerized deployment
• Safety, red teaming, ethical auditing, and serving models as secure APIs

Inside the Book: ✓ 17 chapters covering the entire LLM development lifecycle
✓ Code samples, command-line utilities, and training scripts you can run today
✓ Integration with leading open-source tools: PyTorch, Transformers, Weights & Biases, DeepSpeed
✓ Cloud deployment strategies on AWS, GCP, and Azure
✓ Real-world applications in healthcare, finance, education, and more

Why This Book?
Unlike high-level theory or limited tutorials, this is a practical engineer’s playbook—backed by real code, implementation best practices, and detailed walkthroughs.
If you’ve ever wanted to build your own GPT, optimize a billion-parameter model, or deploy a private LLM API, this is the definitive guide.