Building LLMs for Production :Enhancing LLM Abilities And Reliability With Prompting, Fine-Tuning And RAG

By:

Bouchard L.F

Contributor(s):

Peters L

Language: English Publication details: SPD 2024Description: 453ISBN:

9789355427830

Summary: Table of Contents Acknowlegment...............................................................................................................................i Preface ............................................................................................................................................ii List of Abbreviations...................................................................................................................... v Introduction .................................................................................................................................... 1 Chapter I: Introduction to LLMs.................................................................................................... 1 What are Large Language Models .................................................................................... 8 Key LLM Terminologies................................................................................................... 8 From Language Models to Large Language Models ...................................................... 19 History of NLP/LLMs..................................................................................................... 26 Recap............................................................................................................................... 30 Chapter II: LLM Architectures and Landscape............................................................................ 33 Understanding Transformer ............................................................................................ 34 Transformer Model’s Design Choices............................................................................. 42 The Generative Pre-trained Transformer (GPT) Architecture ........................................ 50 Introduction to Large Multimodal Models...................................................................... 54 Proprietary vs. Open Models vs. Open-Source Language Models.................................. 59 Applications and Use-Cases of LLMs............................................................................. 66 Recap............................................................................................................................... 70 Chapter III: LLMs in Practice ...................................................................................................... 73 Understanding Hallucinations and Bias .......................................................................... 74 Evaluating LLM Performance......................................................................................... 77 Controlling LLM Outputs ............................................................................................... 82 Pretraining and Fine-Tuning LLMs................................................................................. 84 Recap............................................................................................................................... 87 Chapter IV: Introduction to Prompting ........................................................................................ 89 Prompting and Prompt Engineering ................................................................................ 90 Prompting Techniques..................................................................................................... 92 Bad Prompt Practices .................................................................................................... 100 Tips for Effective Prompt Engineering ......................................................................... 103 Recap............................................................................................................................. 105 Chapter V: Introduction to LangChain & LlamaIndex .............................................................. 107 LangChain Introduction ................................................................................................ 108 LangChain Agents & Tools Overview.......................................................................... 110 Building LLM-Powered Applications with LangChain ................................................111 Building a News Articles Summarizer ..........................................................................115 LlamaIndex Introduction ...............................................................................................123 LangChain vs. LlamaIndex vs. OpenAI Assistants.......................................................132 Recap .............................................................................................................................134 Chapter VI: Prompting with LangChain.....................................................................................137 What are LangChain Prompt Templates........................................................................138 Few Shot Prompts and Example Selectors....................................................................145 Managing Outputs with Output Parsers.........................................................................152 Improving Our News Articles Summarizer...................................................................163 Creating Knowledge Graphs from Textual Data: Unveiling Hidden Connections .......172 Recap .............................................................................................................................177 Chapter VII: Retrieval-Augmented Generation..........................................................................179 Retrieval-Augmented Generation..................................................................................180 LangChain’s Indexes and Retrievers.............................................................................181 Data Ingestion................................................................................................................186 What are Text Splitters and Why They are Useful........................................................191 Tutorial: A Customer Support Q&A Chatbot................................................................201 Embeddings...................................................................................................................207 What are LangChain Chains..........................................................................................215 Tutorial: A YouTube Video Summarizer Using Whisper and LangChain....................222 Tutorial: A Voice Assistant for Your Knowledge Base ................................................232 Preventing Undesirable Outputs With the Self-Critique Chain.....................................245 Recap .............................................................................................................................254 Chapter VIII: Advanced RAG....................................................................................................257 Prompting vs. Fine-Tuning vs. RAG.............................................................................258 Advanced RAG Techniques with LlamaIndex..............................................................260 Production-Ready RAG Solutions with LlamaIndex ....................................................271 RAG - Metrics & Evaluation.........................................................................................276 LangChain’s LangSmith – Introduction ........................................................................294 Recap .............................................................................................................................299 Chapter IX: Agents.....................................................................................................................301 What are Agents: Large Models as Reasoning Engines................................................302 An Overview of AutoGPT and BabyAGI .....................................................................308 The Agent Simulation Projects in LangChain............................................................... 322 Tutorial: Building Agents for Analysis Report Creation............................................... 327 Tutorial: Query and Summarize a DB with LlamaIndex .............................................. 336 Building Agents with OpenAI Assistants...................................................................... 345 LangChain OpenGPT.................................................................................................... 355 Tutorial: Multimodal Financial Document Analysis from PDFs.................................. 357 Recap............................................................................................................................. 371 Chapter X: Fine-Tuning ............................................................................................................. 373 Techniques for Fine-Tuning LLMs............................................................................... 374 Low-Rank Adaptation (LoRA) ..................................................................................... 375 Practical Example: SFT with LoRA.............................................................................. 377 Using SFT for Financial Sentiment............................................................................... 388 Fine-Tuning a Cohere LLM with Medical Data............................................................ 396 Reinforcement Learning from Human Feedback .......................................................... 405 Tutorial: Improving LLMs with RLHF......................................................................... 409 Recap............................................................................................................................. 429 Chapter XI: Deployment ............................................................................................................ 431 Challenges of LLM Deployment................................................................................... 432 Model Quantization....................................................................................................... 433 Model Pruning............................................................................................................... 440 Deploying an LLM on a Cloud CPU............................................................................. 442 Recap............................................................................................................................. 448 Conclusion.................................................................................................................................. 451

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	Books	Cummins College of Engineering for Women Pune				006.35 BOU (Browse shelf(Opens below))					Available (not for issue)			CCEP-BK-67514

Table of Contents
Acknowlegment...............................................................................................................................i
Preface ............................................................................................................................................ii
List of Abbreviations...................................................................................................................... v
Introduction .................................................................................................................................... 1
Chapter I: Introduction to LLMs.................................................................................................... 1
What are Large Language Models .................................................................................... 8
Key LLM Terminologies................................................................................................... 8
From Language Models to Large Language Models ...................................................... 19
History of NLP/LLMs..................................................................................................... 26
Recap............................................................................................................................... 30
Chapter II: LLM Architectures and Landscape............................................................................ 33
Understanding Transformer ............................................................................................ 34
Transformer Model’s Design Choices............................................................................. 42
The Generative Pre-trained Transformer (GPT) Architecture ........................................ 50
Introduction to Large Multimodal Models...................................................................... 54
Proprietary vs. Open Models vs. Open-Source Language Models.................................. 59
Applications and Use-Cases of LLMs............................................................................. 66
Recap............................................................................................................................... 70
Chapter III: LLMs in Practice ...................................................................................................... 73
Understanding Hallucinations and Bias .......................................................................... 74
Evaluating LLM Performance......................................................................................... 77
Controlling LLM Outputs ............................................................................................... 82
Pretraining and Fine-Tuning LLMs................................................................................. 84
Recap............................................................................................................................... 87
Chapter IV: Introduction to Prompting ........................................................................................ 89
Prompting and Prompt Engineering ................................................................................ 90
Prompting Techniques..................................................................................................... 92
Bad Prompt Practices .................................................................................................... 100
Tips for Effective Prompt Engineering ......................................................................... 103
Recap............................................................................................................................. 105
Chapter V: Introduction to LangChain & LlamaIndex .............................................................. 107
LangChain Introduction ................................................................................................ 108
LangChain Agents & Tools Overview.......................................................................... 110
Building LLM-Powered Applications with LangChain ................................................111
Building a News Articles Summarizer ..........................................................................115
LlamaIndex Introduction ...............................................................................................123
LangChain vs. LlamaIndex vs. OpenAI Assistants.......................................................132
Recap .............................................................................................................................134
Chapter VI: Prompting with LangChain.....................................................................................137
What are LangChain Prompt Templates........................................................................138
Few Shot Prompts and Example Selectors....................................................................145
Managing Outputs with Output Parsers.........................................................................152
Improving Our News Articles Summarizer...................................................................163
Creating Knowledge Graphs from Textual Data: Unveiling Hidden Connections .......172
Recap .............................................................................................................................177
Chapter VII: Retrieval-Augmented Generation..........................................................................179
Retrieval-Augmented Generation..................................................................................180
LangChain’s Indexes and Retrievers.............................................................................181
Data Ingestion................................................................................................................186
What are Text Splitters and Why They are Useful........................................................191
Tutorial: A Customer Support Q&A Chatbot................................................................201
Embeddings...................................................................................................................207
What are LangChain Chains..........................................................................................215
Tutorial: A YouTube Video Summarizer Using Whisper and LangChain....................222
Tutorial: A Voice Assistant for Your Knowledge Base ................................................232
Preventing Undesirable Outputs With the Self-Critique Chain.....................................245
Recap .............................................................................................................................254
Chapter VIII: Advanced RAG....................................................................................................257
Prompting vs. Fine-Tuning vs. RAG.............................................................................258
Advanced RAG Techniques with LlamaIndex..............................................................260
Production-Ready RAG Solutions with LlamaIndex ....................................................271
RAG - Metrics & Evaluation.........................................................................................276
LangChain’s LangSmith – Introduction ........................................................................294
Recap .............................................................................................................................299
Chapter IX: Agents.....................................................................................................................301
What are Agents: Large Models as Reasoning Engines................................................302
An Overview of AutoGPT and BabyAGI .....................................................................308
The Agent Simulation Projects in LangChain............................................................... 322
Tutorial: Building Agents for Analysis Report Creation............................................... 327
Tutorial: Query and Summarize a DB with LlamaIndex .............................................. 336
Building Agents with OpenAI Assistants...................................................................... 345
LangChain OpenGPT.................................................................................................... 355
Tutorial: Multimodal Financial Document Analysis from PDFs.................................. 357
Recap............................................................................................................................. 371
Chapter X: Fine-Tuning ............................................................................................................. 373
Techniques for Fine-Tuning LLMs............................................................................... 374
Low-Rank Adaptation (LoRA) ..................................................................................... 375
Practical Example: SFT with LoRA.............................................................................. 377
Using SFT for Financial Sentiment............................................................................... 388
Fine-Tuning a Cohere LLM with Medical Data............................................................ 396
Reinforcement Learning from Human Feedback .......................................................... 405
Tutorial: Improving LLMs with RLHF......................................................................... 409
Recap............................................................................................................................. 429
Chapter XI: Deployment ............................................................................................................ 431
Challenges of LLM Deployment................................................................................... 432
Model Quantization....................................................................................................... 433
Model Pruning............................................................................................................... 440
Deploying an LLM on a Cloud CPU............................................................................. 442
Recap............................................................................................................................. 448
Conclusion.................................................................................................................................. 451

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