Project Title

Awesome-Multimodal-Large-Language-Models — A Curated Collection of Resources on Multimodal Large Language Models

Overview

Awesome-Multimodal-Large-Language-Models is a comprehensive repository that serves as a hub for the latest advances in multimodal large language models (MLLMs). It offers a curated collection of surveys, projects, and benchmarks that are shaping the future of AI in multimodal contexts. This project stands out for its focus on both the technical and practical aspects of MLLMs, providing a one-stop resource for researchers and developers alike.

Key Features

Survey on Multimodal Large Language Models: A detailed survey providing insights into the current state and future directions of MLLMs.
VITA Project: An open-source interactive omni multimodal LLM with a powerful and real-time version, VITA-1.5, offering both code and demo experiences.
Long-VITA: A model capable of processing over 1M visual tokens, setting new standards in video analysis.
MM-RLHF: A project aligning MLLMs with human preferences through a high-quality dataset and a new alignment algorithm.
MME-Survey: A comprehensive survey on the evaluation of multimodal LLMs, jointly introduced by leading teams in the field.

Use Cases

Research and Development: For AI researchers and developers looking to stay updated with the latest in multimodal LLMs and leverage the provided resources for their projects.
Educational Purposes: As a learning tool for students and professionals to understand the complexities and applications of MLLMs.
Benchmarking and Evaluation: For organizations needing standardized benchmarks to evaluate the performance of their multimodal models.

Advantages

Comprehensiveness: Covers a wide range of resources, from surveys to code, providing a holistic view of the MLLM landscape.
Community Engagement: Includes links to WeChat groups for community discussions, fostering collaboration and knowledge sharing.
Practical Applications: Offers real-time interactive demos and datasets for practical application and testing.

Limitations / Considerations

License Information: The license type is unknown, which might affect the usability for commercial projects.
Technical Complexity: The projects and resources are highly technical and may require a strong background in AI and machine learning to fully utilize.

Hugging Face Transformers: A library of state-of-the-art pre-trained models for Natural Language Processing, differing in its focus on text-based models rather than multimodal approaches.
MMBench: A benchmark for evaluating multimodal models, similar in purpose but with a different set of evaluations and datasets.
LLaVA: A project focused on large vision-and-language models, offering a different perspective on multimodal AI compared to the broader scope of Awesome-Multimodal-Large-Language-Models.

Basic Information

GitHub: https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models
Stars: 16,273
License: Unknown
Last Commit: 2025-09-16

📊 Project Information

Project Name: Awesome-Multimodal-Large-Language-Models
GitHub URL: https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models
Programming Language: Unknown
⭐ Stars: 16,273
🍴 Forks: 1,055
📅 Created: 2023-05-19
🔄 Last Updated: 2025-09-16

🏷️ Project Topics

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🎮 Online Demos

🎥 Video Tutorials

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Awesome-Multimodal-Large-Language-Models

Project Description