This repository provides code and notebooks for the "Quick Start Guide to Large Language Models - Second Edition" book, targeting engineers and researchers looking to implement LLM applications. It offers practical examples for semantic search, prompt engineering, RAG, fine-tuning, and production deployment.

How It Works

The project utilizes Jupyter notebooks to demonstrate various LLM techniques, including fine-tuning Transformer models like BERT and Llama, building RAG pipelines, and creating AI agents. It covers both OpenAI and open-source models, showcasing practical applications and advanced customization methods.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Navigate to the notebooks directory to open Jupyter notebooks.
• Some notebooks may require specific datasets found in the data directory.
• Official book: Quick Start Guide to Large Language Models - Second Edition

Highlighted Details

• Demonstrates fine-tuning for app review classification using BERT and OpenAI models.
• Includes examples of building recommendation engines with custom embeddings.
• Features a step-by-step guide to constructing Visual Question Answering (VQA) systems.
• Covers advanced topics like Reinforcement Learning from Human Feedback (RLHF) for model alignment and knowledge distillation for efficient deployment.

Maintenance & Community

The repository is maintained by Sinan Öztürk. Further AI/LLM content is available via his newsletter "AI Office Hours" and podcast "Practically Intelligent."

Licensing & Compatibility

The repository's license is not explicitly stated in the README.

Limitations & Caveats

This repository is intended for educational purposes and accompanies a book; in-depth explanations are found in the book itself. Specific hardware or API key requirements for running certain notebooks are not detailed.