Hands-On Large Language Models Jay Alammar, Maarten Grootendorst

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Spis treści książki

• Preface
  • An Intuition-First Philosophy
  • Prerequisites
  • Book Structure
    • Part I: Understanding Language Models
    • Part II: Using Pretrained Language Models
    • Part III: Training and Fine-Tuning Language Models
  • Hardware and Software Requirements
  • API Keys
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Understanding Language Models
• 1. An Introduction to Large Language Models
  • What Is Language AI?
  • A Recent History of Language AI
    • Representing Language as a Bag-of-Words
    • Better Representations with Dense Vector Embeddings
    • Types of Embeddings
    • Encoding and Decoding Context with Attention
    • Attention Is All You Need
    • Representation Models: Encoder-Only Models
    • Generative Models: Decoder-Only Models
    • The Year of Generative AI
  • The Moving Definition of a Large Language Model
  • The Training Paradigm of Large Language Models
  • Large Language Model Applications: What Makes Them So Useful?
  • Responsible LLM Development and Usage
  • Limited Resources Are All You Need
  • Interfacing with Large Language Models
    • Proprietary, Private Models
    • Open Models
    • Open Source Frameworks
  • Generating Your First Text
  • Summary
• 2. Tokens and Embeddings
  • LLM Tokenization
    • How Tokenizers Prepare the Inputs to the Language Model
    • Downloading and Running an LLM
    • How Does the Tokenizer Break Down Text?
    • Word Versus Subword Versus Character Versus Byte Tokens
    • Comparing Trained LLM Tokenizers
      • BERT base model (uncased) (2018)
      • BERT base model (cased) (2018)
      • GPT-2 (2019)
      • Flan-T5 (2022)
      • GPT-4 (2023)
      • StarCoder2 (2024)
      • Galactica
      • Phi-3 (and Llama 2)
    • Tokenizer Properties
      • Tokenization methods
      • Tokenizer parameters
      • The domain of the data
  • Token Embeddings
    • A Language Model Holds Embeddings for the Vocabulary of Its Tokenizer
    • Creating Contextualized Word Embeddings with Language Models
  • Text Embeddings (for Sentences and Whole Documents)
  • Word Embeddings Beyond LLMs
    • Using pretrained Word Embeddings
    • The Word2vec Algorithm and Contrastive Training
  • Embeddings for Recommendation Systems
    • Recommending Songs by Embeddings
    • Training a Song Embedding Model
  • Summary
• 3. Looking Inside Large Language Models
  • An Overview of Transformer Models
    • The Inputs and Outputs of a Trained Transformer LLM
    • The Components of the Forward Pass
    • Choosing a Single Token from the Probability Distribution (Sampling/Decoding)
    • Parallel Token Processing and Context Size
    • Speeding Up Generation by Caching Keys and Values
    • Inside the Transformer Block
      • The feedforward neural network at a glance
      • The attention layer at a glance
      • Attention is all you need
      • How attention is calculated
      • Self-attention: Relevance scoring
      • Self-attention: Combining information
  • Recent Improvements to the Transformer Architecture
    • More Efficient Attention
      • Local/sparse attention
      • Multi-query and grouped-query attention
      • Optimizing attention: From multi-head to multi-query to grouped query
      • Flash Attention
    • The Transformer Block
    • Positional Embeddings (RoPE)
    • Other Architectural Experiments and Improvements
  • Summary
• II. Using Pretrained Language Models
• 4. Text Classification
  • The Sentiment of Movie Reviews
  • Text Classification with Representation Models
  • Model Selection
  • Using a Task-Specific Model
  • Classification Tasks That Leverage Embeddings
    • Supervised Classification
    • What If We Do Not Have Labeled Data?
  • Text Classification with Generative Models
    • Using the Text-to-Text Transfer Transformer
    • ChatGPT for Classification
  • Summary
• 5. Text Clustering and Topic Modeling
  • ArXivs Articles: Computation and Language
  • A Common Pipeline for Text Clustering
    • Embedding Documents
    • Reducing the Dimensionality of Embeddings
    • Cluster the Reduced Embeddings
    • Inspecting the Clusters
  • From Text Clustering to Topic Modeling
    • BERTopic: A Modular Topic Modeling Framework
    • Adding a Special Lego Block
      • KeyBERTInspired
      • Maximal marginal relevance
    • The Text Generation Lego Block
  • Summary
• 6. Prompt Engineering
  • Using Text Generation Models
    • Choosing a Text Generation Model
    • Loading a Text Generation Model
    • Controlling Model Output
      • Temperature
      • top_p
  • Intro to Prompt Engineering
    • The Basic Ingredients of a Prompt
    • Instruction-Based Prompting
  • Advanced Prompt Engineering
    • The Potential Complexity of a Prompt
    • In-Context Learning: Providing Examples
    • Chain Prompting: Breaking up the Problem
  • Reasoning with Generative Models
    • Chain-of-Thought: Think Before Answering
    • Self-Consistency: Sampling Outputs
    • Tree-of-Thought: Exploring Intermediate Steps
  • Output Verification
    • Providing Examples
    • Grammar: Constrained Sampling
  • Summary
• 7. Advanced Text Generation Techniques and Tools
  • Model I/O: Loading Quantized Models with LangChain
  • Chains: Extending the Capabilities of LLMs
    • A Single Link in the Chain: Prompt Template
    • A Chain with Multiple Prompts
  • Memory: Helping LLMs to Remember Conversations
    • Conversation Buffer
    • Windowed Conversation Buffer
    • Conversation Summary
  • Agents: Creating a System of LLMs
    • The Driving Power Behind Agents: Step-by-step Reasoning
    • ReAct in LangChain
  • Summary
• 8. Semantic Search and Retrieval-Augmented Generation
  • Overview of Semantic Search and RAG
  • Semantic Search with Language Models
    • Dense Retrieval
      • Dense retrieval example
        • Getting the text archive and chunking it
        • Embedding the text chunks
        • Building the search index
        • Search the index
      • Caveats of dense retrieval
      • Chunking long texts
        • One vector per document
        • Multiple vectors per document
      • Nearest neighbor search versus vector databases
      • Fine-tuning embedding models for dense retrieval
    • Reranking
      • Reranking example
      • Open source retrieval and reranking with sentence transformers
      • How reranking models work
    • Retrieval Evaluation Metrics
      • Scoring a single query with average precision
      • Scoring across multiple queries with mean average precision
  • Retrieval-Augmented Generation (RAG)
    • From Search to RAG
    • Example: Grounded Generation with an LLM API
    • Example: RAG with Local Models
      • Loading the generation model
      • Loading the embedding model
      • The RAG prompt
    • Advanced RAG Techniques
      • Query rewriting
      • Multi-query RAG
      • Multi-hop RAG
      • Query routing
      • Agentic RAG
    • RAG Evaluation
  • Summary
• 9. Multimodal Large Language Models
  • Transformers for Vision
  • Multimodal Embedding Models
    • CLIP: Connecting Text and Images
    • How Can CLIP Generate Multimodal Embeddings?
    • OpenCLIP
  • Making Text Generation Models Multimodal
    • BLIP-2: Bridging the Modality Gap
    • Preprocessing Multimodal Inputs
      • Preprocessing images
      • Preprocessing text
    • Use Case 1: Image Captioning
    • Use Case 2: Multimodal Chat-Based Prompting
  • Summary
• III. Training and Fine-Tuning Language Models
• 10. Creating Text Embedding Models
  • Embedding Models
  • What Is Contrastive Learning?
  • SBERT
  • Creating an Embedding Model
    • Generating Contrastive Examples
    • Train Model
    • In-Depth Evaluation
    • Loss Functions
      • Cosine similarity
      • Multiple negatives ranking loss
  • Fine-Tuning an Embedding Model
    • Supervised
    • Augmented SBERT
  • Unsupervised Learning
    • Transformer-Based Sequential Denoising Auto-Encoder
    • Using TSDAE for Domain Adaptation
  • Summary
• 11. Fine-Tuning Representation Models for Classification
  • Supervised Classification
    • Fine-Tuning a Pretrained BERT Model
    • Freezing Layers
  • Few-Shot Classification
    • SetFit: Efficient Fine-Tuning with Few Training Examples
    • Fine-Tuning for Few-Shot Classification
  • Continued Pretraining with Masked Language Modeling
  • Named-Entity Recognition
    • Preparing Data for Named-Entity Recognition
    • Fine-Tuning for Named-Entity Recognition
  • Summary
• 12. Fine-Tuning Generation Models
  • The Three LLM Training Steps: Pretraining, Supervised Fine-Tuning, and Preference Tuning
  • Supervised Fine-Tuning (SFT)
    • Full Fine-Tuning
    • Parameter-Efficient Fine-Tuning (PEFT)
      • Adapters
      • Low-Rank Adaptation (LoRA)
      • Compressing the model for (more) efficient training
  • Instruction Tuning with QLoRA
    • Templating Instruction Data
    • Model Quantization
    • LoRA Configuration
    • Training Configuration
    • Training
    • Merge Weights
  • Evaluating Generative Models
    • Word-Level Metrics
    • Benchmarks
    • Leaderboards
    • Automated Evaluation
    • Human Evaluation
  • Preference-Tuning / Alignment / RLHF
  • Automating Preference Evaluation Using Reward Models
    • The Inputs and Outputs of a Reward Model
    • Training a Reward Model
      • Reward model training dataset
      • Reward model training step
    • Training No Reward Model
  • Preference Tuning with DPO
    • Templating Alignment Data
    • Model Quantization
    • Training Configuration
    • Training
  • Summary
• Afterword
• Index

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