Awesome Multimodal Large Language Models (MLLM) is a curated collection of resources in the field of multimodal AI. This repository covers essential aspects of MLLMs including research papers, open-source implementations, datasets, and applications.
Key Components
Research Papers
Latest research in multimodal architectures, training methods, and applications.
Open Source Projects
Selected implementations including model architectures, training frameworks, and inference engines.
Core Technologies
- Modal fusion architectures (early, mid, late fusion)
- Vision encoders (CNN, ViT, CLIP)
- Language model integration
- Training and fine-tuning methods
Major Models
- Open source: LLaVA, MiniGPT-4, BLIP-2
- Commercial: GPT-4V, Gemini, Claude 3
- Specialized models for healthcare, scientific documents, and code generation
Applications
- Visual question answering
- Content generation
- Document understanding
- Code generation from images
Challenges & Future Trends
Addressing modal alignment, computational efficiency, and data quality while expanding into new applications and research directions.