Generative AI on AWS Building Context-Aware Multimodal Reasoning Applications
Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML pract...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
[Sebastopol, California] :
O'Reilly Media, Inc
2023.
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| Edición: | First edtion |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009784594106719 |
Tabla de Contenidos:
- Cover
- Copyright
- Table of Contents
- Preface
- Conventions Used in This Book
- Using Code Examples
- O'Reilly Online Learning
- How to Contact Us
- Acknowledgments
- Chris
- Antje
- Shelbee
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 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.
