This repository provides a comprehensive course on Generative AI with Large Language Models (LLMs), targeting developers with a foundational understanding of LLMs. It aims to equip learners with the knowledge to deploy generative AI in real-world applications, make informed business decisions, and build prototypes efficiently.

How It Works

The course covers the entire LLM lifecycle, from data gathering and model selection to performance evaluation and deployment. It delves into the transformer architecture, training methodologies, and fine-tuning techniques for adapting LLMs to specific use cases. Emphasis is placed on empirical scaling laws for optimizing models based on dataset size, compute budget, and inference requirements.

Quick Start & Requirements

This is an educational course, not a deployable software package. The content is structured into weekly modules with learning objectives, theoretical explanations, and practical labs. No specific installation or runtime requirements are listed as it's a curriculum.

• Resources: Course Syllabus (implied by repository structure)

Highlighted Details

• Deep understanding of generative AI fundamentals and LLM lifecycle.
• Detailed explanation of transformer architecture and training processes.
• Practical application of scaling laws for model optimization.
• Coverage of state-of-the-art training, tuning, inference, and deployment methods.
• Discussion of business challenges and opportunities presented by generative AI.

Maintenance & Community

No information on maintainers, community channels, or roadmap is provided in the README.

Licensing & Compatibility

The repository content is not explicitly licensed. It appears to be educational material, and its use for commercial or closed-source applications would require clarification.

Limitations & Caveats

This repository contains course material and does not provide a runnable codebase or framework. The practical labs require separate setup and execution of LLM tools and models, which are not included here.