LLMs: From Foundations to Production

Tutorial Status

A hands-on tutorial series for mastering Large Language Models (LLMs) – featuring practical examples, code implementations, and real-world applications. This series takes you from foundational concepts to building production-ready LLM applications.

About This Tutorial Series

This comprehensive tutorial series is designed to provide practical, hands-on experience with LLM development and deployment. Each tutorial combines theoretical concepts with practical implementations, real-world examples, and coding exercises.

Prerequisites

Don’t worry if you’re not an expert in these areas - we’ll review key concepts as needed throughout the tutorials.

Roadmap

image

  • Intro to Large Language Models
    • Fundamentals of Language Models
    • LLM Capabilities and Applications
  • Tokenization
    • Understanding Tokenization Fundamentals
    • BPE Tokenization
    • Working with Hugging Face Tokenizers
    • Building Custom Tokenizers
    • GPT Tokenization Approach
    • Multilingual Tokenization Strategies
  • Embeddings
    • Word and Token Embeddings
    • Word2Vec Architecture
    • GloVe Embeddings
    • Contextual Embeddings
    • Fine-tuning LLM Embeddings
    • Semantic Search Implementation

image

  • Neural Network Foundations for LLMs
    • Neural Network Basics
    • Activation Functions, Gradients, and Backpropagation
    • Loss Functions and Regularization Strategies
    • Optimization Algorithms and Hyperparameter Tuning
  • Traditional Language Models
    • N-gram Language Models and Smoothing Techniques
    • Feedforward Neural Language Models
    • Recurrent Neural Network Language Models
      • Long Short-Term Memory (LSTM) Networks
      • Gated Recurrent Units (GRUs)
      • Bidirectional and Multilayer RNNs
  • The Transformer Architecture
    • Attention Mechanisms and Self-Attention
    • Multi-Head Attention and Positional Encodings
    • Transformer Encoder and Decoder Stacks
    • Residual Connections and Layer Normalization
    • Implementing the Transformer from Scratch

image

  • Data Preparation
    • LLM Training Data Collection
    • Text Cleaning for LLMs
    • Data Filtering and Deduplication
    • Creating Training Datasets
    • Dataset Curation and Quality Control
    • Dataset Annotation Workflows
    • Hugging Face Hub Dataset Management
    • Data Preparation Techniques for Large-Scale NLP Applications
  • Pre-Training Large Language Models
    • Model Architecture Selection
    • Unsupervised Pre-Training Objectives
      • Masked Language Modeling (MLM)
      • Permutation Language Modeling (PLM)
      • Replaced Token Detection (RTD)
      • Span-based Masking
      • Prefix Language Modeling
    • Efficient Pre-Training Techniques
      • Dynamic Masking and Whole Word Masking
      • Large Batch Training and Learning Rate Scheduling
      • Curriculum Learning
      • Progressive Training Strategies
    • Training Infrastructure
      • Distributed Training Setup
      • Mixed Precision Training
      • Multi-device Optimization
    • Training Optimization
      • Weight Initialization
      • AdamW Optimizer
      • Learning Rate Scheduling
    • Precision Formats
      • FP16/BF16 Training
      • FP8 Optimization
    • Distributed Training
      • Data Parallel Training
      • ZeRO Optimization
      • Distributed Data Processing
    • Scaling Laws and Model Architecture Variants
  • Post-Training Datasets
    • Dataset Storage and Chat Templates
    • Generating Synthetic Training Data
    • Dataset Augmentation Techniques
    • Quality Control and Filtering
  • Supervised Fine-Tuning
    • Post-Training Techniques
    • Parameter Efficient Fine-Tuning (PEFT)
    • LoRA Implementation
    • Chat Model Fine-tuning
    • Distributed Fine-tuning
  • Preference Alignment
    • Reinforcement Learning Fundamentals
    • Deep Reinforcement Learning for LLMs
    • Policy Optimization Methods
    • Proximal Policy Optimization (PPO)
    • Direct Preference Optimization (DPO)
    • Rejection sampling
  • Model Architecture Variants
    • Mixture of Experts (MoE)
    • Sparse Architectures
    • Mamba Architecture
    • Sliding Window Attention Models
    • Hybrid Transformer-RNN Architectures
    • GraphFormers and Graph-based LLMs
  • Reasoning
    • Reasoning Fundamentals
    • Chain of Thought
    • Group Relative Policy Optimization (GRPO)
  • Model Evaluation
    • Benchmarking LLM Models
    • Assessing Performance (Human evaluation)
    • Bias and Safety Testing

image

  • Quantization
    • Quantization Fundamentals
    • Post-Training Quantization (PTQ)
    • Quantization-Aware Training (QAT)
    • GGUF Format and llama.cpp Implementation
    • Advanced Techniques: GPTQ and AWQ
    • Integer Quantization Methods
    • Modern Approaches: SmoothQuant and ZeroQuant
  • Inference Optimization
    • Flash Attention
    • KV Cache Implementation
    • Test-Time Preference Optimization (TPO)
    • Compression Methods to Enhance LLM Performance
  • Running LLMs
    • Using LLM APIs
    • Building Memory-Enabled Chatbots
    • Working with Open-Source Models
    • Prompt Engineering
    • Structured Outputs
    • Deploying Models Locally
    • Creating Interactive Demos
    • Setting Up Production Servers
    • Serving Open Source LLMs in a Production Environment
    • Developing REST APIs
    • Managing Concurrent Users
    • Test-Time Autoscaling
    • Batching for Model Deployment

image

  • Retrieval Augmented Generation
    • Ingesting documents
    • Chunking Strategies
    • Embedding models
    • Vector databases
    • Retrieval Implementation
    • RAG Pipeline Building
    • Graph RAG Techniques
    • Constructing and Optimizing Knowledge Graphs
    • Intelligent Document Processing (IDP) with RAG
  • Tool Use & AI Agents
    • Function Calling and Tool Usage
    • Agent Implementation
    • Planning Systems
    • Agentic RAG
    • Multi-agent Orchestration
  • Text-to-SQL Systems
    • Fundamentals of Text-to-SQL
    • Few-Shot Prompting Techniques
    • In-Context Learning and Self-Correction
    • Schema-Aware Approaches
    • Fine-Tuning Strategies for SQL Generation
    • Hybrid Neural-Symbolic Methods
    • Benchmarking and Evaluation
  • Multimodal
    • Working with Multi-Modal LLMs, Including Text, Audio Input/Output, and Images
    • Transfer Learning & Pre-trained Models
    • Multimodal Transformers
    • Vision-Language Models
    • Multimodal Attention
    • Feature Fusion
    • Image Captioning
    • Visual QA Systems
    • Text-to-Image Generation
    • Multimodal Chatbots
    • Joint Image-Text Representations
  • Securing LLMs
    • Prompt Injection Attacks
    • Data/Prompt Leaking
    • Jailbreaking Techniques
    • Training Data Poisoning
    • Backdoor Attacks
    • Model Theft Prevention
    • Fairness in LLMs
    • Bias Detection and Mitigation
    • Responsible AI Development
    • Personal Information Masking
    • Reconstruction Methods
  • Large Language Model Operations (LLMOps)
    • Hugging Face Hub Integration
      • Model Card Creation
      • Model Sharing
      • Version Control
    • LLM Observability Tools
    • Techniques for Debugging and Monitoring
    • Docker, OpenShift, CI/CD
    • Dependency Management and Containerization
    • Apache Spark usage for LLM Inference
  • Model Enhancement
    • Context Window Expansion
    • Model Merging
    • Knowledge Distillation

How to Follow Along

  1. Follow tutorials sequentially
  2. Complete the coding exercises
  3. Build the suggested projects
  4. Experiment with the provided examples

Contributing

We welcome contributions! If you’d like to:

  • Fix my mistakes
  • Improve existing content
  • Share your implementations

Please submit a pull request or open an issue.

Community Support

  • Join our Telegram Channel for discussions
  • Check out the Issues section for help
  • Share your implementations in Discussions

Acknowledgments

Thanks to all contributors and the AI/ML community for their valuable input and code contributions.

Let’s start building with LLMs! 🚀