Name: Decoding Large Language Models
Author: Irena Cronin
ISBN: 9781835081808

微信读书书城Decoding Large Language Models

首页我的书架登录

翻译完成

切换

Decoding Large Language Models

Irena Cronin

扉页
版权信息
+书签
文前
Contributors About the author
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
Part 1 The Foundations of Large Language Models (LLMs)
Chapter 1 LLM Architecture
The anatomy of a language model
Training data
Tokenization
Neural network architecture
Embeddings
Transformers and attention mechanisms
Types of attention
Decoder blocks
Parameters
Fine-tuning
Outputs
Applications
Ethical considerations
Safety and moderation
User interaction
Recurrent neural networks (RNNs) and their limitations
Overview of RNNs
Limitations of RNNs
Addressing the limitations
Comparative analysis – Transformer versus RNN models
Summary
Chapter 2 How LLMs Make Decisions
Decision-making in LLMs – probability and statistical analysis
Probabilistic modeling and statistical analysis
Training on large datasets
Contextual understanding
Machine learning algorithms
Feedback loops
Uncertainty and error
From input to output – understanding LLM response generation
Input processing
Model architecture
Decoding and generation
Iterative generation
Post-processing
Challenges and limitations in LLM decision-making
Evolving decision-making – advanced techniques and future directions
Advanced techniques in LLM decision-making
Future directions for LLM decision-making
Challenges and considerations
Summary
Part 2 Mastering LLM Development
Chapter 3 The Mechanics of Training LLMs
Data – preparing the fuel for LLMs
Data collection
Data cleaning
Tokenization
Annotation
Data augmentation
Preprocessing
Validation split
Feature engineering
Balancing the dataset
Data format
Setting up your training environment
Hardware infrastructure
Software and tools
Other items
Hyperparameter tuning – finding the sweet spot
Challenges in training LLMs – overfitting, underfitting, and more
Summary
Chapter 4 Advanced Training Strategies
Transfer learning and fine-tuning in practice
Transfer learning
Fine-tuning
Practical implementation of transfer learning and fine-tuning
Case study – enhancing clinical diagnosis with transfer learning and fine-tuning in NLP
Curriculum learning – teaching LLMs effectively
Key concepts in curriculum learning
Benefits of curriculum learning
Additional considerations
Implementing curriculum learning
Challenges in curriculum learning
Case study – curriculum learning in training LLMs for legal document analysis
Multitasking and continual learning models
Multitasking models
Continual learning models
Integration of multitasking and continual learning
Case study – implementing multitasking and continual learning models for e-commerce personalization
Case study – training an LLM for a specialized domain
Challenges and considerations
Conclusion
Summary
Chapter 5 Fine-Tuning LLMs for Specific Applications
Incorporating LoRA and PEFT for efficient fine-tuning
LoRA
PEFT
Integrating LoRA, PEFT, PPO, and DPO into fine-tuning practices
Understanding the needs of NLP applications
Computational efficiency
Domain adaptability
Robustness to noise
Scalability
Multilinguality
User interaction
Ethical considerations
Interoperability
Tailoring LLMs for chatbots and conversational agents
Understanding the domain and intent
Personalization and context management
Natural language generation
Performance optimization
Ethical and privacy considerations
Continuous improvement
Customizing LLMs for language translation
Data preparation
Model training
Handling linguistic nuances
Quality and consistency
Dealing with limitations
Ethical and practical considerations
Continuous improvement
Sentiment analysis and beyond – fine-tuning for nuanced understanding
The basics of sentiment analysis
Challenges in sentiment analysis
Fine-tuning for nuanced understanding
Evaluation and adjustment
Practical applications
Ethical considerations
Beyond sentiment analysis
Summary
Chapter 6 Testing and Evaluating LLMs
Metrics for measuring LLM performance
Quantitative metrics
Qualitative metrics
Setting up rigorous testing protocols
Defining test cases
Benchmarking
Automated test suites
Continuous integration
Stress testing
A/B testing
Regression testing
Version control
User testing
Ethical and bias testing
Documentation
Legal and compliance checks
Human-in-the-loop – incorporating human judgment in evaluation
Ethical considerations and bias migration
Summary
Part 3 Deployment and Enhancing LLM Performance
Chapter 7 Deploying LLMs in Production
Deployment strategies for LLMs
Choosing the right model
Integration approach
Environment setup
Data pipeline integration
Scalability and deployment considerations
Hardware and computational resources
Scalability strategies
Cloud versus on-premises solutions
Load balancing and resource allocation
Security best practices for LLM integration
Data privacy and protection
Access control and authentication
Implementation considerations
Regular security audits
Continuous monitoring and maintenance
Continuous monitoring
Maintenance practices
Summary
Chapter 8 Strategies for Integrating LLMs
Evaluating compatibility – aligning LLMs with current systems
Technical specifications assessment
Understanding data formats
Compatibility with programming languages, APIs, and frameworks
Aligning with operational workflows
Automation of tasks
Customization needs
Outcome achievement
Seamless integration techniques
Incremental implementation
API and microservices architecture
Data pipeline management
Monitoring and feedback loops
Customizing LLMs for system-specific requirements
Fine-tuning
Adding domain-specific knowledge
User interface adaptation
Addressing security and privacy concerns in integration
Summary
Chapter 9 Optimization Techniques for Performance
Quantization – doing more with less
Model size reduction
Inference speed
Power efficiency
Hardware compatibility
A minimal impact on accuracy
Trade-offs
Pruning – trimming the fat from LLMs
The identification of redundant weights
Weight removal
Sparsity
Efficiency
The impact on performance
Structured versus unstructured pruning
Pruning schedules
Fine-tuning
Knowledge distillation – transferring wisdom efficiently
Teacher-student model paradigm
The transfer of knowledge
Case study – optimizing the ExpressText LLM for mobile deployment
Background
Objective
Methodology
Results
Challenges
Solutions
Conclusion
Summary
Chapter 10 Advanced Optimization and Efficiency
Advanced hardware acceleration techniques
Tensor cores
FPGAs’ versatility and adaptability
Emerging technologies
System-level optimizations
Efficient data representation and storage
Speeding up inference without compromising quality
Distillation
Optimized algorithms
Additional methods
Balancing cost and performance in LLM deployment
Cloud versus on-premises
Model serving choices
Cost-effective and sustainable deployment
Summary
Part 4 Issues, Practical Insights, and Preparing for the Future
Chapter 11 LLM Vulnerabilities, Biases, and Legal Implications
LLM vulnerabilities – identifying and mitigating risks
Identification of security risks
Mitigation strategies
Continual learning and updates
Collaboration with security experts
Ethical hacking and penetration testing
Confronting biases in LLMs
Legal challenges in LLM deployment and usage
Intellectual property rights and AI-generated content
Liability issues and LLM outputs
Regulatory landscape and compliance for LLMs
Ethical considerations and future outlook
Transparency
Accountability
Future outlook
Continuous ethical assessments
Hypothetical case study – bias mitigation in AI for hiring platforms
Initial issue
Bias mitigation approach
Outcome
Key takeaways
Summary
Chapter 12 Case Studies – Business Applications and ROI
Implementing LLMs in customer service enhancement
Background
Objective
Implementation of LLMs
Results
Challenges
Future developments
Conclusion
LLMs in marketing – strategy and content optimization
Background
Objective
Implementation of LLMs
Results
Challenges
Future developments
Conclusion
Operational efficiency through LLMs – automation and analysis
Background
Objective
Implementation of LLMs
Results
Challenges
Future developments
Conclusion
Assessing ROI – financial and operational impacts of LLMs
Financial impact assessment
Operational impact assessment
ROI calculation
Conclusion
Summary
Chapter 13 The Ecosystem of LLM Tools and Frameworks
Surveying the landscape of AI tools
Open source versus proprietary – choosing the right tools
Open source tools for LLMs
Proprietary tools for LLMs
Integrating LLMs with existing software stacks
The role of cloud providers in NLP
Summary
Chapter 14 Preparing for GPT-5 and Beyond
What to expect from the next generation of LLMs
Enhanced understanding and contextualization
Improved language and multimodal abilities
Greater personalization
Increased efficiency and speed
Advanced reasoning and problem-solving
Broader knowledge and learning
Ethical and bias mitigation
Improved interaction with other AI systems
More robust data privacy and security
Customizable and scalable deployment
Regulatory compliance and transparency
Accessible AI for smaller businesses
Enhanced interdisciplinary applications
Getting ready for GPT-5 – infrastructure and skillsets
Potential breakthroughs and challenges ahead
Strategic planning for future LLMs
Summary
Chapter 15 Conclusion and Looking Forward
Key takeaways from the book
Foundational architecture and decision-making
Training mechanics and advanced strategies
Fine-tuning, testing, and deployment
Optimization, vulnerabilities, and future prospects
Continuing education and resources for technical leaders
Final thoughts — embracing the LLM revolution
Index
Why subscribe?
Other Books You May Enjoy
Packt is searching for authors like you
Share Your Thoughts
Download a free PDF copy of this book

简介

版权

Decoding Large Language Models