This repository provides a curated learning path and best practices for understanding and working with Large Language Models (LLMs). It targets individuals new to LLMs, developers, and researchers seeking to deepen their knowledge, offering a structured approach from basic interaction to advanced research and practical implementation. The primary benefit is a consolidated, actionable guide to navigating the rapidly evolving LLM landscape.

How It Works

The project outlines a multi-stage learning process. It begins with experiencing LLMs through readily available platforms, then moves to foundational understanding via courses like Andrew Ng's, and progresses to advanced study through curated research papers and practical coding exercises. The inclusion of a regularly updated list of influential LLM research papers, categorized by topic and rated for importance, is a key feature.

Quick Start & Requirements

• Experience LLMs: Requires scientific internet access (e.g., VPN) to use services like ChatGPT, Claude, or Bard.
• Foundational Learning: Links to courses and resources for prompt engineering and LangChain are provided.
• Advanced Study: Curated GitHub repositories like "Awesome-LLM" are recommended for paper discovery.
• Code Practice: Suggestions include calling LLM APIs, self-hosting LLMs, and fine-tuning.

Highlighted Details

• Structured learning path from beginner to advanced.
• Curated list of influential LLM research papers with ratings.
• Focus on practical application through API usage, self-hosting, and fine-tuning.
• Includes resources for LLMs in recommendation systems.

Maintenance & Community

The project mentions weekly sharing meetings and invites contributions via email. Star history is linked.

Licensing & Compatibility

The repository itself is not explicitly licensed in the provided text. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The repository is a collection of links and curated resources, not a software project with executable code. The "best practices" are observational and may not reflect the most current or universally accepted methods. The project's maintenance status and the frequency of updates beyond the paper list are not detailed.