Generative AI on AWS

Spis treści ebooka

• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • Chris
    • Antje
    • Shelbee
• 1. Generative AI Use Cases, Fundamentals, and Project Life Cycle
  • Use Cases and Tasks
  • Foundation Models and Model Hubs
  • Generative AI Project Life Cycle
  • Generative AI on AWS
  • Why Generative AI on AWS?
  • Building Generative AI Applications on AWS
  • Summary
• 2. Prompt Engineering and In-Context Learning
  • Prompts and Completions
  • Tokens
  • Prompt Engineering
  • Prompt Structure
    • Instruction
    • Context
  • In-Context Learning with Few-Shot Inference
    • Zero-Shot Inference
    • One-Shot Inference
    • Few-Shot Inference
    • In-Context Learning Gone Wrong
    • In-Context Learning Best Practices
  • Prompt-Engineering Best Practices
  • Inference Configuration Parameters
  • Summary
• 3. Large-Language Foundation Models
  • Large-Language Foundation Models
  • Tokenizers
  • Embedding Vectors
  • Transformer Architecture
    • Inputs and Context Window
    • Embedding Layer
    • Encoder
    • Self-Attention
    • Decoder
    • Softmax Output
  • Types of Transformer-Based Foundation Models
  • Pretraining Datasets
  • Scaling Laws
  • Compute-Optimal Models
  • Summary
• 4. Memory and Compute Optimizations
  • Memory Challenges
  • Data Types and Numerical Precision
  • Quantization
    • fp16
    • bfloat16
    • fp8
    • int8
  • Optimizing the Self-Attention Layers
    • FlashAttention
    • Grouped-Query Attention
  • Distributed Computing
    • Distributed Data Parallel
    • Fully Sharded Data Parallel
    • Performance Comparison of FSDP over DDP
  • Distributed Computing on AWS
    • Fully Sharded Data Parallel with Amazon SageMaker
    • AWS Neuron SDK and AWS Trainium
  • Summary
• 5. Fine-Tuning and Evaluation
  • Instruction Fine-Tuning
    • Llama 2-Chat
    • Falcon-Chat
    • FLAN-T5
  • Instruction Dataset
    • Multitask Instruction Dataset
    • FLAN: Example Multitask Instruction Dataset
    • Prompt Template
    • Convert a Custom Dataset into an Instruction Dataset
  • Instruction Fine-Tuning
    • Amazon SageMaker Studio
    • Amazon SageMaker JumpStart
    • Amazon SageMaker Estimator for Hugging Face
  • Evaluation
    • Evaluation Metrics
    • Benchmarks and Datasets
  • Summary
• 6. Parameter-Efficient Fine-Tuning
  • Full Fine-Tuning Versus PEFT
  • LoRA and QLoRA
    • LoRA Fundamentals
    • Rank
    • Target Modules and Layers
    • Applying LoRA
    • Merging LoRA Adapter with Original Model
    • Maintaining Separate LoRA Adapters
    • Full-Fine Tuning Versus LoRA Performance
    • QLoRA
  • Prompt Tuning and Soft Prompts
  • Summary
• 7. Fine-Tuning with Reinforcement Learning from Human Feedback
  • Human Alignment: Helpful, Honest, and Harmless
  • Reinforcement Learning Overview
  • Train a Custom Reward Model
    • Collect Training Dataset with Human-in-the-Loop
    • Sample Instructions for Human Labelers
    • Using Amazon SageMaker Ground Truth for Human Annotations
    • Prepare Ranking Data to Train a Reward Model
    • Train the Reward Model
  • Existing Reward Model: Toxicity Detector by Meta
  • Fine-Tune with Reinforcement Learning from Human Feedback
    • Using the Reward Model with RLHF
    • Proximal Policy Optimization RL Algorithm
    • Perform RLHF Fine-Tuning with PPO
    • Mitigate Reward Hacking
    • Using Parameter-Efficient Fine-Tuning with RLHF
  • Evaluate RLHF Fine-Tuned Model
    • Qualitative Evaluation
    • Quantitative Evaluation
    • Load Evaluation Model
    • Define Evaluation-Metric Aggregation Function
    • Compare Evaluation Metrics Before and After
  • Summary
• 8. Model Deployment Optimizations
  • Model Optimizations for Inference
    • Pruning
    • Post-Training Quantization with GPTQ
    • Distillation
  • Large Model Inference Container
  • AWS Inferentia: Purpose-Built Hardware for Inference
  • Model Update and Deployment Strategies
    • A/B Testing
    • Shadow Deployment
  • Metrics and Monitoring
  • Autoscaling
    • Autoscaling Policies
    • Define an Autoscaling Policy
  • Summary
• 9. Context-Aware Reasoning Applications Using RAG and Agents
  • Large Language Model Limitations
    • Hallucination
    • Knowledge Cutoff
  • Retrieval-Augmented Generation
    • External Sources of Knowledge
    • RAG Workflow
    • Document Loading
    • Chunking
    • Document Retrieval and Reranking
    • Prompt Augmentation
  • RAG Orchestration and Implementation
    • Document Loading and Chunking
    • Embedding Vector Store and Retrieval
    • Retrieval Chains
    • Reranking with Maximum Marginal Relevance
  • Agents
    • ReAct Framework
    • Program-Aided Language Framework
  • Generative AI Applications
  • FMOps: Operationalizing the Generative AI Project Life Cycle
    • Experimentation Considerations
    • Development Considerations
    • Production Deployment Considerations
  • Summary
• 10. Multimodal Foundation Models
  • Use Cases
  • Multimodal Prompt Engineering Best Practices
  • Image Generation and Enhancement
    • Image Generation
    • Image Editing and Enhancement
  • Inpainting, Outpainting, Depth-to-Image
    • Inpainting
    • Outpainting
    • Depth-to-Image
  • Image Captioning and Visual Question Answering
    • Image Captioning
    • Content Moderation
    • Visual Question Answering
  • Model Evaluation
    • Text-to-Image Generative Tasks
    • Forward Diffusion
    • Nonverbal Reasoning
  • Diffusion Architecture Fundamentals
    • Forward Diffusion
    • Reverse Diffusion
    • U-Net
  • Stable Diffusion 2 Architecture
    • Text Encoder
    • U-Net and Diffusion Process
    • Text Conditioning
    • Cross-Attention
    • Scheduler
    • Image Decoder
  • Stable Diffusion XL Architecture
    • U-Net and Cross-Attention
    • Refiner
    • Conditioning
  • Summary
• 11. Controlled Generation and Fine-Tuning with Stable Diffusion
  • ControlNet
  • Fine-Tuning
    • DreamBooth
    • DreamBooth and PEFT-LoRA
    • Textual Inversion
  • Human Alignment with Reinforcement Learning from Human Feedback
  • Summary
• 12. Amazon Bedrock: Managed Service for Generative AI
  • Bedrock Foundation Models
    • Amazon Titan Foundation Models
    • Stable Diffusion Foundation Models from Stability AI
  • Bedrock Inference APIs
  • Large Language Models
    • Generate SQL Code
    • Summarize Text
    • Embeddings
  • Fine-Tuning
  • Agents
  • Multimodal Models
    • Create Images from Text
    • Create Images from Images
  • Data Privacy and Network Security
  • Governance and Monitoring
  • Summary
• Index

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