This repository serves as a comprehensive, curated collection of resources for Multimodal Large Language Models (MLLMs). It targets researchers, engineers, and practitioners interested in the rapidly evolving field of MLLMs, offering a centralized hub for datasets, tuning techniques, evaluation methods, and foundational models to accelerate development and understanding.

How It Works

The repository functions as a dynamic knowledge base, meticulously organizing and linking to a vast array of academic papers, datasets, and open-source projects related to MLLMs. It categorizes resources by key areas such as multimodal instruction tuning, in-context learning, visual reasoning, and foundational models, providing structured access to cutting-edge research and practical tools.

Quick Start & Requirements

This repository is a curated list of resources, not a runnable software package. No installation or execution commands are applicable.

Highlighted Details

• Extensive categorization of papers and datasets covering multimodal instruction tuning, in-context learning, chain-of-thought reasoning, and visual reasoning.
• Includes a "Milestone Papers" section tracking the historical development of LLMs and MLLMs.
• Features a comprehensive list of open-source LLM projects, training frameworks, and deployment tools.
• Provides links to numerous benchmarks and leaderboards for evaluating LLM performance.

Maintenance & Community

The repository is actively maintained, with frequent updates to reflect the latest advancements in MLLMs. It aims to stay synchronized with the forefront of research.

Licensing & Compatibility

The repository itself is a collection of links and information; licensing is dependent on the individual projects and datasets referenced within. Users must consult the licenses of linked resources for usage rights.

Limitations & Caveats

As a curated list, this repository does not provide direct functionality or code. Users must independently locate, download, and integrate the referenced models, datasets, and tools. The rapid pace of MLLM development means the information may require continuous verification against original sources.