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Spis treści

Designing Large Language Model Applications eBook -- spis treści

• Preface
  • Who This Book Is For
  • How This Book Is Structured
  • What This Book Is Not About
  • How to Read the Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. LLM Ingredients
• 1. Introduction
  • Defining LLMs
  • A Brief History of LLMs
    • Early Years
    • The Modern LLM Era
  • The Impact of LLMs
  • LLM Usage in the Enterprise
  • Prompting
    • Zero-Shot Prompting
    • Few-Shot Prompting
    • Chain-of-Thought Prompting
    • Prompt Chaining
    • Adversarial Prompting
  • Accessing LLMs Through an API
  • Strengths and Limitations of LLMs
  • Building Your First Chatbot Prototype
  • From Prototype to Production
  • Summary
• 2. Pre-Training Data
  • Ingredients of an LLM
  • Pre-Training Data Requirements
  • Popular Pre-Training Datasets
  • Synthetic Pre-Training Data
  • Training Data Preprocessing
    • Data Filtering and Cleaning
    • Selecting Quality Documents
      • Token-distribution K-L divergence
      • Classifier-based approaches
      • Perplexity for quality selection
    • Deduplication
    • Removing Personally Identifiable Information
      • PII detection
      • PII remediation
    • Training Set Decontamination
    • Data Mixtures
  • Effect of Pre-Training Data on Downstream Tasks
  • Bias and Fairness Issues in Pre-Training Datasets
  • Summary
• 3. Vocabulary and Tokenization
  • Vocabulary
  • Tokenizers
  • Tokenization Pipeline
    • Normalization
    • Pre-Tokenization
    • Tokenization
    • Byte Pair Encoding
      • Training stage
      • Inference stage
    • WordPiece
      • Postprocessing
    • Special Tokens
  • Summary
• 4. Architectures and Learning Objectives
  • Preliminaries
  • Representing Meaning
  • The Transformer Architecture
    • Self-Attention
    • Positional Encoding
    • Feedforward Networks
    • Layer Normalization
  • Loss Functions
  • Intrinsic Model Evaluation
  • Transformer Backbones
    • Encoder-Only Architectures
    • Encoder-Decoder Architectures
    • Decoder-Only Architectures
    • Mixture of Experts
  • Learning Objectives
    • Full Language Modeling
    • Prefix Language Modeling
    • Masked Language Modeling
    • Which Learning Objectives Are Better?
  • Pre-Training Models
  • Summary
• II. Utilizing LLMs
• 5. Adapting LLMs to Your Use Case
  • Navigating the LLM Landscape
    • Who Are the LLM providers?
    • Model Flavors
      • Instruct-models
      • Chat-models
      • Long-context models
      • Domain-adapted or task-adapted models
    • Open Source LLMs
  • How to Choose an LLM for Your Task
    • Open Source Versus Proprietary LLMs
    • LLM Evaluation
      • Eleuther AI LM Evaluation Harness
      • Hugging Face Open LLM Leaderboard
      • HELM
      • Elo Rating
      • Interpreting benchmark results
  • Loading LLMs
    • Hugging Face Accelerate
    • Ollama
    • LLM Inference APIs
  • Decoding Strategies
    • Greedy Decoding
    • Beam Search
    • Top-k Sampling
    • Top-p Sampling
  • Running Inference on LLMs
  • Structured Outputs
  • Model Debugging and Interpretability
  • Summary
• 6. Fine-Tuning
  • The Need for Fine-Tuning
  • Fine-Tuning: A Full Example
    • Learning Algorithms Parameters
      • Optimizers
      • Learning rates
      • Learning schedules
    • Memory Optimization Parameters
      • Gradient checkpointing
      • Gradient accumulation
      • Quantization
    • Regularization Parameters
      • Label smoothing
      • Noise Embeddings
    • Batch Size
    • Parameter-Efficient Fine-Tuning
    • Working with Reduced Precision
    • Putting It All Together
  • Fine-Tuning Datasets
    • Utilizing Publicly Available Instruction-Tuning Datasets
    • LLM-Generated Instruction-Tuning Datasets
  • Summary
• 7. Advanced Fine-Tuning Techniques
  • Continual Pre-Training
    • Replay (Memory)
    • Parameter Expansion
  • Parameter-Efficient Fine-Tuning
    • Adding New Parameters
      • Bottleneck adapters
      • Prefix-tuning
      • Prompt tuning
    • Subset Methods
  • Combining Multiple Models
    • Model Ensembling
      • PairRanker
      • GenFuser
    • Model Fusion
    • Adapter Merging
  • Summary
• 8. Alignment Training and Reasoning
  • Defining Alignment Training
  • Reinforcement Learning
    • Types of Human Feedback
    • RLHF Example
  • Hallucinations
  • Mitigating Hallucinations
    • Self-Consistency
    • Chain-of-Actions
    • Recitation
    • Sampling Methods for Addressing Hallucination
    • Decoding by Contrasting Layers
  • In-Context Hallucinations
  • Hallucinations Due to Irrelevant Information
  • Reasoning
    • Deductive Reasoning
    • Inductive Reasoning
    • Abductive Reasoning
    • Common Sense Reasoning
  • Inducing Reasoning in LLMs
    • Verifiers for Improving Reasoning
      • Iterative backprompting
      • Top-k guessing
    • Inference-Time Computation
      • Repeated sampling
      • Search
    • Fine-Tuning for Reasoning
  • Summary
• 9. Inference Optimization
  • LLM Inference Challenges
  • Inference Optimization Techniques
  • Techniques for Reducing Compute
    • K-V Caching
    • Early Exit
      • Sequence-level early exit
      • Token-level early exit
    • Knowledge Distillation
      • Distillation data preparation
      • Distillation
  • Techniques for Accelerating Decoding
    • Speculative Decoding
    • Parallel Decoding
  • Techniques for Reducing Storage Needs
    • Symmetric Quantization
    • Asymmetric Quantization
  • Summary
• III. LLM Application Paradigms
• 10. Interfacing LLMs with External Tools
  • LLM Interaction Paradigms
    • Passive Approach
    • The Explicit Approach
    • The Autonomous Approach
  • Defining Agents
  • Agentic Workflow
  • Components of an Agentic System
    • Models
    • Tools
      • Web search
      • API connectors
      • Code interpreter
      • Database connectors
    • Data Stores
      • Prompt repository
      • Session memory
      • Tools data
    • Agent Loop Prompt
      • ReAct
      • Reflection
    • Guardrails and Verifiers
      • Safety Guardrails
      • Verification modules
    • Agent Orchestration Software
  • Summary
• 11. Representation Learning and Embeddings
  • Introduction to Embeddings
  • Semantic Search
  • Similarity Measures
  • Fine-Tuning Embedding Models
    • Base Models
    • Training Dataset
    • Loss Functions
  • Instruction Embeddings
  • Optimizing Embedding Size
    • Matryoshka Embeddings
    • Binary and Integer Embeddings
    • Product Quantization
  • Chunking
    • Sliding Window Chunking
    • Metadata-Aware Chunking
    • Layout-Aware Chunking
    • Semantic Chunking
    • Late Chunking
  • Vector Databases
  • Interpreting Embeddings
  • Summary
• 12. Retrieval-Augmented Generation
  • The Need for RAG
  • Typical RAG Scenarios
  • Deciding When to Retrieve
  • The RAG Pipeline
    • Rewrite
    • Retrieve
      • Generative retrieval
      • Tightly-coupled retrievers
      • GraphRAG
    • Rerank
      • Query likelihood model (QLM)
      • LLM distillation for ranking
    • Refine
      • Summarization
      • Chain-of-note
    • Insert
    • Generate
  • RAG for Memory Management
  • RAG for Selecting In-Context Training Examples
  • RAG for Model Training
  • Limitations of RAG
  • RAG Versus Long Context
  • RAG Versus Fine-Tuning
  • Summary
• 13. Design Patterns and System Architecture
  • Multi-LLM Architectures
    • LLM Cascades
    • Routers
    • Task-Specialized LLMs
  • Programming Paradigms
    • DSPy
      • Modules
      • Optimizers
    • LMQL
  • Summary
• Index