This repository is a comprehensive, community-driven curated list of resources for Large Language Models (LLMs), covering everything from data processing and fine-tuning to inference, evaluation, and agent development. It serves as a valuable knowledge base for researchers, engineers, and practitioners looking to stay updated with the rapidly evolving LLM landscape.

How It Works

The project acts as a central hub, aggregating links to GitHub repositories, papers, tutorials, courses, and tools. It categorizes these resources into logical sections such as Data, Fine-Tuning, Inference, Evaluation, RAG, Agents, and more, allowing users to quickly navigate and discover relevant information. The continuous updates by the community ensure the list remains current with the latest advancements.

Quick Start & Requirements

This is a curated list, not a runnable application. No installation or specific requirements are needed to browse the resources.

Highlighted Details

• Extensive coverage of LLM lifecycle: data preparation, training, inference, and evaluation.
• Dedicated sections for emerging areas like Retrieval-Augmented Generation (RAG) and AI Agents.
• Includes links to technical reports, research papers, and practical tutorials.
• Features a broad range of tools and frameworks for both foundational LLM work and application development.

Maintenance & Community

The project is actively maintained by the community, with contributions from various individuals and organizations. It encourages community input for updates and additions.

Licensing & Compatibility

The repository itself is a collection of links and does not have a specific license. The licenses of the linked projects vary.

Limitations & Caveats

As a curated list, the quality and maintenance of individual linked projects can vary. Users should exercise due diligence when adopting any specific tool or framework. The sheer volume of resources may require significant time to explore thoroughly.