LLMs: Syllabus

Mastering Large Language Models (LLMs): From Pretraining to Deployment with Python

Large Language Models (LLMs) are transforming AI, enabling capabilities in text generation, summarization, chatbots, and code generation. This book takes a hands-on approach to LLMs, covering model pretraining, fine-tuning, optimization, and deployment. By the end of this book, readers will have deep expertise in implementing LLMs using Python, Hugging Face, TensorFlow, and PyTorch.

Module 1: Introduction to Large Language Models (LLMs)#

• What are Large Language Models? Evolution and Use Cases
• Key breakthroughs: GPT, BERT, T5, LLaMA, and beyond
• Differences between Small, Medium, and Large LLMs
• Setting up the Python environment for LLM development (Hugging Face, TensorFlow, PyTorch, Transformers library)

Module 2: Understanding Transformer Architectures#

• Revisiting Neural Networks and RNNs for NLP
• The Self-Attention Mechanism and Multi-Head Attention
• Positional Encoding and the Importance of Context
• Deep dive into Transformer-based architectures (GPT, BERT, T5, LLaMA)

Module 3: Pretraining LLMs from Scratch#

• Understanding Tokenization (WordPiece, Byte-Pair Encoding, SentencePiece)
• Data collection and preprocessing for LLMs
• Training an LLM on a custom dataset with TensorFlow/PyTorch
• Using Distributed Training for Large-Scale Models

Module 4: Fine-Tuning Pretrained LLMs#

• Transfer learning with LLMs (Zero-shot, Few-shot, and Fine-tuning)
• Fine-tuning GPT and BERT models for specific tasks
• Parameter-efficient fine-tuning (LoRA, QLoRA, PEFT)
• Avoiding overfitting and catastrophic forgetting

Module 5: Reinforcement Learning with Human Feedback (RLHF)#

• Introduction to RLHF and why it improves model performance
• Training LLMs with preference datasets
• Implementing RLHF with PPO (Proximal Policy Optimization)
• Real-world applications: Aligning models to human feedback

Module 6: Optimizing and Scaling LLMs#

• Reducing inference costs with model quantization (INT8, INT4, GPTQ)
• Model distillation: Making LLMs smaller and faster
• Parallel and distributed training techniques
• Efficient memory management for large-scale LLMs

Module 7: LLM Deployment and Inference Optimization#

• Deploying LLMs using Flask, FastAPI, and gRPC
• Hosting LLMs on cloud services (AWS Sagemaker, GCP Vertex AI, Hugging Face Spaces)
• Optimizing inference latency with TensorRT and ONNX
• Implementing API rate limiting and caching for scalability

Module 8: Using LLMs for Real-World Applications#

• Chatbot development with LangChain and RAG (Retrieval-Augmented Generation)
• Summarization, Question Answering, and Code Generation
• Multimodal LLMs for image, text, and audio generation
• Automating workflows with LLMs in enterprise applications

Module 9: Evaluating and Debugging LLMs#

• Understanding Perplexity, BLEU, ROUGE, and Accuracy Metrics
• Detecting hallucinations and factual inconsistencies
• Ethical considerations: Bias, fairness, and safety in LLMs
• Improving explainability with interpretability tools

Module 10: Advanced Topics in LLM Research#

• OpenAI’s GPT-4 vs. Google’s PaLM vs. Meta’s LLaMA
• Prompt engineering and chain-of-thought reasoning
• Autonomous AI agents (AutoGPT, BabyAGI)
• Future directions: LLMs and AGI (Artificial General Intelligence)

Hands-On Projects

Project 1: Fine-Tuning GPT for Text Summarization#

• Fine-tune GPT-3 or GPT-4 on a custom dataset
• Implement extractive and abstractive summarization
• Deploy as an API using FastAPI

Project 2: Building a Custom Chatbot with LangChain#

• Integrate LLMs with knowledge bases for intelligent responses
• Use embeddings and vector search with FAISS
• Deploy the chatbot as a cloud-based service

Project 3: Low-Latency LLM Deployment with Model Quantization#

• Convert an LLM to INT8 using GPTQ
• Benchmark inference speed and latency improvements
• Deploy on an edge device using TensorRT

Project 4: Retrieval-Augmented Generation (RAG) with LLMs#

• Implement RAG using FAISS and OpenAI embeddings
• Fine-tune the model for improved document retrieval
• Deploy as a scalable search assistant

Project 5: Reinforcement Learning with Human Feedback (RLHF) for LLM Alignment#

• Train a preference dataset for RLHF
• Implement PPO for reward-based model tuning
• Analyze model improvements post-training

References#

• Hugging Face Transformers
• OpenAI GPT-4 API
• DeepMind Reinforcement Learning
• LangChain for LLM Apps
• Model Quantization Techniques