This repository is a comprehensive, curated list of resources related to Large Language Models (LLMs), Vision Foundation Models, and AI-Generated Content (AIGC). It serves as an "awesome list" for researchers, developers, and enthusiasts looking to explore the rapidly evolving landscape of AI, providing links to frameworks, models, datasets, applications, and learning materials.

How It Works

The repository is structured into broad categories covering LLMs, Vision Foundation Models, AIGC, application development platforms, fine-tuning frameworks, RAG frameworks, inference frameworks, and deployment engines. It meticulously lists and categorizes numerous projects, papers, and blogs, offering a centralized hub for discovering and understanding state-of-the-art AI technologies and their implementations.

Quick Start & Requirements

This repository is a curated list and does not require installation or execution. It provides links to external projects and resources.

Highlighted Details

• Extensive coverage of LLMs, including major models like GPT, Llama, Gemma, and Claude, alongside numerous Chinese-specific models.
• Detailed sections on Vision Foundation Models, featuring prominent projects like SAM, LLaVA, and DINO, with a focus on multimodal capabilities.
• A broad overview of AIGC, encompassing text-to-image, text-to-video (Sora), and audio generation models.
• Categorization of application development platforms, fine-tuning tools, RAG frameworks, and inference/deployment engines, offering a complete ecosystem view.

Maintenance & Community

The repository is actively maintained, with frequent updates reflecting the fast-paced development in the AI field. It links to various community resources, including Hugging Face and ModelScope, fostering collaboration.

Licensing & Compatibility

The repository itself is a list of links and does not have a specific license. The licensing of the linked projects varies, with many projects being open-source (e.g., Apache 2.0, MIT) and some having commercial use clauses. Users must check the individual project licenses for compatibility.

Limitations & Caveats

As a curated list, the quality and maintenance of linked projects can vary. The sheer volume of information may be overwhelming for newcomers, and the rapid pace of AI development means some links or information might become outdated.