Large Language Model-Based Solutions

SHREYAS SUBRAMANIAN, PhD, is a principal data scientist at AWS, one of the largest organizations building and providing large language models for enterprise use. He is currently advising both internal Amazon teams and large enterprise customers on building, tuning, and deploying Generative AI applications at scale. Shreyas runs machine learning-focused cost optimization workshops, helping them reduce the costs of machine learning applications on the cloud. Shreyas also actively participates in cutting-edge research and development of advanced training, tuning and deployment techniques for foundation models.

• Introduction xixChapter 1: Introduction 1Overview of GenAI Applications and Large Language Models 1The Rise of Large Language Models 1Neural Networks, Transformers, and Beyond 2GenAI vs. LLMs: What’s the Difference? 5The Three-Layer GenAI Application Stack 6The Infrastructure Layer 6The Model Layer 7The Application Layer 8Paths to Productionizing GenAI Applications 9Sample LLM-Powered Chat Application 11The Importance of Cost Optimization 12Cost Assessment of the Model Inference Component 12Cost Assessment of the Vector Database Component 19Benchmarking Setup and Results 20Other Factors to Consider 23Cost Assessment of the Large Language Model Component 24Summary 27Chapter 2: Tuning Techniques for Cost Optimization 29Fine-Tuning and Customizability 29Basic Scaling Laws You Should Know 30Parameter-Efficient Fine-Tuning Methods 32Adapters Under the Hood 33Prompt Tuning 34Prefix Tuning 36P-tuning 39IA3 40Low-Rank Adaptation 44Cost and Performance Implications of PEFT Methods 46Summary 48Chapter 3: Inference Techniques for Cost Optimization 49Introduction to Inference Techniques 49Prompt Engineering 50Impact of Prompt Engineering on Cost 50Estimating Costs for Other Models 52Clear and Direct Prompts 53Adding Qualifying Words for Brief Responses 53Breaking Down the Request 54Example of Using Claude for PII Removal 55Conclusion 59Providing Context 59Examples of Providing Context 60RAG and Long Context Models 60Recent Work Comparing RAG with Long Content Models 61Conclusion 62Context and Model Limitations 62Indicating a Desired Format 63Example of Formatted Extraction with Claude 63Trade-Off Between Verbosity and Clarity 66Caching with Vector Stores 66What Is a Vector Store? 66How to Implement Caching Using Vector Stores 66Conclusion 69Chains for Long Documents 69What Is Chaining? 69Implementing Chains 69Example Use Case 70Common Components 70Tools That Implement Chains 72Comparing Results 76Conclusion 76Summarization 77Summarization in the Context of Cost and Performance 77Efficiency in Data Processing 77Cost-Effective Storage 77Enhanced Downstream Applications 77Improved Cache Utilization 77Summarization as a Preprocessing Step 77Enhanced User Experience 77Conclusion 77Batch Prompting for Efficient Inference 78Batch Inference 78Experimental Results 80Using the accelerate Library 81Using the DeepSpeed Library 81Batch Prompting 82Example of Using Batch Prompting 83Model Optimization Methods 83Quantization 83Code Example 84Recent Advancements: GPTQ 85Parameter-Efficient Fine-Tuning Methods 85Recap of PEFT Methods 85Code Example 86Cost and Performance Implications 87Summary 88References 88Chapter 4: Model Selection and Alternatives 89Introduction to Model Selection 89Motivating Example: The Tale of Two Models 89The Role of Compact and Nimble Models 90Examples of Successful Smaller Models 91Quantization for Powerful but Smaller Models 91Text Generation with Mistral 7B 93Zephyr 7B and Aligned Smaller Models 94CogVLM for Language-Vision Multimodality 95Prometheus for Fine-Grained Text Evaluation 96Orca 2 and Teaching Smaller Models to Reason 98Breaking Traditional Scaling Laws with Gemini and Phi 99Phi 1, 1.5, and 2 B Models 100Gemini Models 102Domain-Specific Models 104Step 1 - Training Your Own Tokenizer 105Step 2 - Training Your Own Domain-Specific Model 107More References for Fine-Tuning 114Evaluating Domain-Specific Models vs. Generic Models 115The Power of Prompting with General-Purpose Models 120Summary 122Chapter 5: Infrastructure and Deployment Tuning Strategies 123Introduction to Tuning Strategies 123Hardware Utilization and Batch Tuning 124Memory Occupancy 126Strategies to Fit Larger Models in Memory 128KV Caching 130PagedAttention 131How Does PagedAttention Work? 131Comparisons, Limitations, and Cost Considerations 131AlphaServe 133How Does AlphaServe Work? 133Impact of Batching 134Cost and Performance Considerations 134S3: Scheduling Sequences with Speculation 134How Does S3 Work? 135Performance and Cost 135Streaming LLMs with Attention Sinks 136Fixed to Sliding Window Attention 137Extending the Context Length 137Working with Infinite Length Context 137How Does StreamingLLM Work? 138Performance and Results 139Cost Considerations 139Batch Size Tuning 140Frameworks for Deployment Configuration Testing 141Cloud-Native Inference Frameworks 142Deep Dive into Serving Stack Choices 142Batching Options 143Options in DJL Serving 144High-Level Guidance for Selecting Serving Parameters 146Automatically Finding Good Inference Configurations 146Creating a Generic Template 148Defining a HPO Space 149Searching the Space for Optimal Configurations 151Results of Inference HPO 153Inference Acceleration Tools 155TensorRT and GPU Acceleration Tools 156CPU Acceleration Tools 156Monitoring and Observability 157LLMOps and Monitoring 157Why Is Monitoring Important for LLMs? 159Monitoring and Updating Guardrails 160Summary 161Conclusion 163Index 181