{"product_id":"9781638355731","title":"Build a Large Language Model (From Scratch)","description":"\u003cb\u003eHow to implement LLM attention mechanisms and GPT-style transformers. \u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eBuild a Large Language Model (from Scratch)\u003c\/i\u003e bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks.\u003cbr\u003e \u003cbr\u003e \u003ci\u003eBuild a Large Language Model (from Scratch)\u003c\/i\u003e teaches you how to:\u003cbr\u003e \u003cbr\u003e • Plan and code all the parts of an LLM\u003cbr\u003e • Prepare a dataset suitable for LLM training\u003cbr\u003e • Fine-tune LLMs for text classification and with your own data\u003cbr\u003e • Use human feedback to ensure your LLM follows instructions\u003cbr\u003e • Load pretrained weights into an LLM\u003cbr\u003e \u003cbr\u003e \u003ci\u003eBuild a Large Language Model (from Scratch)\u003c\/i\u003e takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e Physicist Richard P. Feynman reportedly said, “I don’t understand anything I can’t build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003ci\u003eBuild a Large Language Model (From Scratch)\u003c\/i\u003e is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you’ll really understand it because you built it yourself!\u003cbr\u003e \u003cbr\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e • Plan and code an LLM comparable to GPT-2\u003cbr\u003e • Load pretrained weights\u003cbr\u003e • Construct a complete training pipeline\u003cbr\u003e • Fine-tune your LLM for text classification\u003cbr\u003e • Develop LLMs that follow human instructions\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cb\u003eSebastian Raschka\u003c\/b\u003e, PhD, is an LLM Research Engineer with over a decade of experience in artificial intelligence. His work spans industry and academia, including implementing LLM solutions as a senior engineer at Lightning AI and teaching as a statistics professor at the University of Wisconsin–Madison.\u003cbr\u003e \u003cbr\u003eSebastian collaborates with Fortune 500 companies on AI solutions and serves on the Open Source Board at University of Wisconsin–Madison. He specializes in LLMs and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations. He is the author of the bestselling books \u003ci\u003eMachine Learning with PyTorch and Scikit-Learn\u003c\/i\u003e, and \u003ci\u003eMachine Learning Q and AI\u003c\/i\u003e.\u003cbr\u003e \u003cbr\u003e The technical editor on this book was \u003cb\u003eDavid Caswell\u003c\/b\u003e.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 1 Understanding large language models\u003cbr\u003e 2 Working with text data\u003cbr\u003e 3 Coding attention mechanisms\u003cbr\u003e 4 Implementing a GPT model from scratch to generate text\u003cbr\u003e 5 Pretraining on unlabeled data\u003cbr\u003e 6 Fine-tuning for classification\u003cbr\u003e 7 Fine-tuning to follow instructions\u003cbr\u003e A Introduction to PyTorch\u003cbr\u003e B References and further reading\u003cbr\u003e C Exercise solutions\u003cbr\u003e D Adding bells and whistles to the training loop\u003cbr\u003e E Parameter-efficient fine-tuning with LoRA","brand":"Manning","offers":[{"title":"Default Title","offer_id":46505679683825,"sku":"9781638355731","price":49.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0674\/5433\/7265\/files\/9781638355731_p0.jpg?v=1765423027","url":"https:\/\/shop.barnesandnoble.com\/products\/9781638355731","provider":"Barnes \u0026 Noble","version":"1.0","type":"link"}