This repository is a curated list of resources for Machine Learning, with a strong focus on Large Language Models (LLMs) and related analytics. It serves as a comprehensive directory for researchers, developers, and practitioners seeking tools, models, datasets, and research papers in the rapidly evolving ML landscape.

How It Works

The project functions as a community-driven, categorized index of open-source projects, libraries, datasets, and research papers. It leverages a hierarchical structure to organize a vast array of ML-related topics, from core LLM models and fine-tuning techniques to specialized areas like multimodal AI, AI DevOps, and audio processing. This broad categorization allows users to quickly navigate and discover relevant resources.

Highlighted Details

• Extensive coverage of LLMs, including open models, tools, GUIs, RAG, agents, and prompt engineering.
• Detailed sections on multimodal AI, covering image, video, and audio models with specific tasks like text-to-video and speech recognition.
• Resources for AI DevOps, ML databases, and safety/responsibility in AI.
• Includes links to benchmarks, leaderboards, datasets, and research papers for performance evaluation and academic exploration.

Maintenance & Community

Contributions are actively welcomed via Pull Requests, indicating an active community effort to maintain and expand the list.

Licensing & Compatibility

The repository itself, as a curated list, does not impose licensing restrictions on the linked resources. However, users must adhere to the licenses of the individual projects and datasets they choose to adopt.

Limitations & Caveats

As a curated list, the quality and maintenance status of individual linked resources can vary. Users should independently verify the relevance, functionality, and licensing of any external project or dataset.