Building Large Language Models from Scratch (Paperback)
Idioma: inglés
Editorial: APress, Berkley, 2026
- Tapa blanda
- Nuevo
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Vendedor de AbeBooks desde 12 de octubre de 2005
Condición: Nuevo
EUR 47,94
Cantidad disponible: 1 disponibles
Añadir al carritoDescripción del artículo del vendedor
Paperback. This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.Starting from the essentials, youll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. Youll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU levelan essential skill for scaling real-world LLMs. Youll also gain mastery over the phases of training that define todays leading models:Pretraining - Building general linguistic and semantic understanding.Midtraining - Expanding domain-specific capabilities and adaptability.Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.By the end of this book, youll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.What Youll LearnHow to configure and optimize your development environment using PyTorchThe mechanics of tokenization, embeddings, normalization, and attention mechanisms.How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.How to integrate custom CUDA kernels to accelerate transformer computations.The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.Who this book is for:Software developers, data scientists, machine learning engineers and AI enthusiasts looking to build their models from scratch. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
N° de ref. del artículo 9798868822964
- Título
- Building Large Language Models from Scratch (Paperback)
- Autor
- Dilyan Grigorov
- Editorial
- APress, Berkley
- Año de publicación
- 2026
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 13
- 9798868822964
This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)―from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.
Starting from the essentials, you’ll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You’ll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU level―an essential skill for scaling real-world LLMs. You’ll also gain mastery over the phases of training that define today’s leading models:
- Pretraining - Building general linguistic and semantic understanding.
- Midtraining - Expanding domain-specific capabilities and adaptability.
- Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.
- Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.
The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.
By the end of this book, you’ll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.
What You’ll Learn
- How to configure and optimize your development environment using PyTorch
- The mechanics of tokenization, embeddings, normalization, and attention mechanisms.
- How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.
- How to integrate custom CUDA kernels to accelerate transformer computations.
- The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.
- Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.
- How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.
Who this book is for:
Software developers, data scientists, machine learning engineers and AI enthusiasts looking to build their models from scratch.
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Dilyan Grigorov is a software developer with a passion for Python software development, generative deep learning & machine learning, data structures, and algorithms. He is an advocate for open source and the Python language itself. He has 16 years of industry experience programming in Python and has spent 5 of those years researching and testing Generative AI solutions. His passion for them stems from his background as an SEO specialist dealing with search engine algorithms daily. He enjoys engaging with the software community, often giving talks at local meetups and larger conferences. In his spare time, he enjoys reading books, hiking in the mountains, taking long walks, playing with his son, and playing the piano.
“Acerca de” puede pertenecer a otra edición de este título.
Grand Eagle Retail
Bensenville, IL, Estados Unidos de America
Vendedor de AbeBooks desde 12 de octubre de 2005
Tarifas de envío en Estados Unidos de America
| Artículo | De 6 a 14 días hábiles | De 6 a 16 días hábiles |
|---|---|---|
| Primer artículo | EUR 0,00 | EUR 0,00 |
Métodos de pago
Información empresarial del vendedor
APOLLO ONLINE CORP.
605 Geddes Street
Wilmington, DE Estados Unidos de America 19805
Condiciones de venta
We guarantee the condition of every book as it¿s described on the Abebooks web sites. If you¿ve changed
your mind about a book that you¿ve ordered, please use the Ask bookseller a question link to contact us
and we¿ll respond within 2 business days.
Books ship from California and Michigan.
Derecho al desistimiento
Si es un consumidor, puede rescindir el contrato de acuerdo con lo siguiente. Por consumidor se entiende cualquier persona física que actúe con fines ajenos a su actividad comercial, empresarial, oficio o profesión.
Información sobre el derecho de desistimiento
Derecho legal de desistimiento
Tiene derecho a rescindir este contrato en un plazo de 14 días sin dar ningún motivo.
El periodo de desistimiento vencerá a los 14 días desde que usted, o un tercero que no sea el transportista e indicado por usted, adquiera la posesión física del último bien o del último lote o pieza.
Para ejercer el derecho de desistimiento, complete de forma electrónica y envíe una declaración clara en nuestro sitio web, desde "Mis compras" en "Mi cuenta". Le enviaremos sin demora un acuse de recibo de dicho desistimiento a través de un soporte duradero (por ejemplo, por correo electrónico).
Para cumplir con el plazo de desistimiento, basta con que envíe su comunicación relativa al ejercicio del derecho de desistimiento antes de que venza el periodo de desistimiento.
Efectos del desistimiento
Si rescinde este contrato, le reembolsaremos todos los pagos que hayamos recibido de usted, incluidos los gastos de envío (excepto los gastos adicionales que surjan si elige un tipo de envío que no sea el tipo de envío estándar más económico que ofrecemos).
Podemos hacer una deducción del reembolso por la pérdida de valor de cualquier bien suministrado, si la pérdida es el resultado de una manipulación innecesaria por su parte.
Efectuaremos el reembolso sin demoras indebidas y, a más tardar, 14 días después de que se nos informe de su decisión de rescindir este contrato.
Efectuaremos el reembolso utilizando el mismo medio de pago que utilizó para la transacción inicial, a menos que haya acordado expresamente lo contrario; en cualquier caso, no incurrirá en ningún cargo como resultado de dicho reembolso.
Podremos retener el reembolso hasta que hayamos recibido los bienes o hasta que nos haya presentado una prueba de que los ha devuelto, lo que ocurra primero.
Deberá devolver los bienes o entregarlos a Grand Eagle Retail, Bensenville, Illinois, U.S.A., sin demoras indebidas y, en cualquier caso, en un plazo máximo de 14 días a partir del día en que nos comunique su desistimiento del presente contrato. El plazo se cumple si devuelve la mercancía antes de que venza el periodo de 14 días. Tendrá que asumir los gastos directos de devolución de los bienes. Usted solo es responsable de la disminución del valor de los bienes como resultado de una manipulación distinta a la necesaria para establecer la naturaleza, las características y el funcionamiento de los bienes.
Excepciones al derecho de desistimiento
El derecho de desistimiento no se aplica a lo siguiente:
- La entrega de periódicos, diarios o revistas, con la excepción de los contratos de suscripción; y
- El suministro de contenido digital que no se proporcione en un soporte tangible (por ejemplo, en un CD o DVD) si, al hacer el pedido, aceptó que podíamos empezar a entregarlo y que no podría desistir una vez iniciada la entrega.
Condiciones de envío
Orders usually ship within 2 business days. All books within the US ship free of charge. Delivery is 4-14 business days anywhere in the United States.
Books ship from California and Michigan.
If your book order is heavy or oversized, we may contact you to let you know extra shipping is required.