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Total Size:
17.5 MB
Info Hash:
6CCC151EFA085D58D746A7AE7E8643F97AD5BB58
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Added:
Dec. 19, 2025, 11:31 a.m.
Stats:
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(Last updated: Dec. 19, 2025, 11:31 a.m.)
| File | Size |
|---|---|
| Code.zip | 692.7 KB |
| Gridin I. The Practical Guide to Large Language Models. Hands-On AI Apps...2025.pdf | 16.8 MB |
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11.6 MB
[134
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57]
2023-10-06
| Uploaded by indexFroggy | Size 11.6 MB | Health [ 134 /57 ] | Added 2023-10-06 |
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17.5 MB
[67
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31]
2025-12-19
| Uploaded by andryold1 | Size 17.5 MB | Health [ 67 /31 ] | Added 2025-12-19 |
NOTE
SOURCE: Gridin I. The Practical Guide to Large Language Models. Hands-On AI Apps...2025
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COVER
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MEDIAINFO
Textbook in PDF format This book is a practical guide to harnessing Hugging Face's powerful transformers library, unlocking access to the largest open-source LLMs. By simplifying complex NLP concepts and emphasizing practical application, it empowers data scientists, machine learning engineers, and NLP practitioners to build robust solutions without delving into theoretical complexities. The book is structured into three parts to facilitate a step-by-step learning journey. Part One covers building production-ready LLM solutions introduces the Hugging Face library and equips readers to solve most of the common NLP challenges without requiring deep knowledge of transformer internals. Part Two focuses on empowering LLMs with RAG and intelligent agents exploring Retrieval-Augmented Generation (RAG) models, demonstrating how to enhance answer quality and develop intelligent agents. Part Three covers LLM advances focusing on expert topics such as model training, principles of transformer architecture and other cutting-edge techniques related to the practical application of language models. Each chapter includes practical examples, code snippets, and hands-on projects to ensure applicability to real-world scenarios. This book bridges the gap between theory and practice, providing professionals with the tools and insights to develop practical and efficient LLM solutions. Introduction Part I: LLM Basics Chapter 1: Discovering Transformers Chapter 2: LLM Internals and Evaluation Chapter 3: Improving Chat Model Responses Part II: Empowering LLM Applications with RAG and Agents Chapter 4: Enriching the Model’s Knowledge with Retrieval-Augmented Chapter 5: Building Agent Systems Part III: LLM Advances Chapter 6: Mastering Model Training Chapter 7: Unpacking the Transformer Architecture Summary
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