Torrent details for "Campesato O.Large Language Models for Developers.A Prompt-based …" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
10.6 MB
Info Hash:
BA5B5C4EAA1DD232A99B9A7A5680A38E4E5F7610
Added By:
Added:
April 22, 2026, 1:23 a.m.
Stats:
|
(Last updated: April 22, 2026, 1:26 a.m.)
| File | Size |
|---|---|
| Campesato O.Large Language Models for Developers.A Prompt-based Exploration 2024.pdf | 10.6 MB |
Name
DL
Uploader
Size
S/L
Added
NOTE
SOURCE: Campesato O.Large Language Models for Developers.A Prompt-based Exploration 2024
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for developers with a foundational understanding of machine learning, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought (ToT) prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture’s attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance. Features: Covers the full lifecycle of working with LLMs, from model selection to deployment Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization Teaches readers to enhance model efficiency with advanced optimization techniques Includes companion files with code and images -- available from the publisher for downloading (with proof of purchase)
×