LAMM

LAMM (pronounced as /læm/, means cute lamb to show appreciation to LLaMA), is a growing open-source community aimed at helping researchers and developers quickly train and evaluate Multi-modal Large Language Models (MLLM), and further build multi-modal AI agents capable of bridging the gap between ideas and execution, enabling seamless interaction between humans and AI machines.

🌏 Project Page

Updates

📆 [2024-03]

1. Ch3Ef is available!
2. Ch3Ef released on Arxiv!
3. Dataset and leaderboard are available!

📆 [2023-12]

1. DepictQA: Depicted Image Quality Assessment based on Multi-modal Language Models released on Arxiv!
2. MP5: A Multi-modal LLM based Open-ended Embodied System in Minecraft released on Arxiv!

📆 [2023-11]

1. ChEF: A comprehensive evaluation framework for MLLM released on Arxiv!
2. Octavius: Mitigating Task Interference in MLLMs by combining Mixture-of-Experts (MoEs) with LoRAs released on Arxiv!
3. Camera ready version of LAMM is available on Arxiv.

📆 [2023-10]

1. LAMM is accepted by NeurIPS2023 Datasets & Benchmark Track! See you in December!

📆 [2023-09]

1. Light training framework for V100 or RTX3090 is available! LLaMA2-based finetuning is also online.
2. Our demo moved to OpenXLab.

📆 [2023-07]

1. Checkpoints & Leaderboard of LAMM on huggingface updated on new code base.
2. Evaluation code for both 2D and 3D tasks are ready.
3. Command line demo tools updated.

📆 [2023-06]

1. LAMM: 2D & 3D dataset & benchmark for MLLM
2. Watch demo video for LAMM at YouTube or Bilibili!
3. Full paper with Appendix is available on Arxiv.
4. LAMM dataset released on Huggingface & OpenDataLab for Research community!',
5. LAMM code is available for Research community!

Paper List

Publications

• LAMM
• Octavius

Preprints

• Assessment of Multimodal Large Language Models in Alignment with Human Values
• ChEF

Citation

LAMM

@article{yin2023lamm,
    title={LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark},
    author={Yin, Zhenfei and Wang, Jiong and Cao, Jianjian and Shi, Zhelun and Liu, Dingning and Li, Mukai and Sheng, Lu and Bai, Lei and Huang, Xiaoshui and Wang, Zhiyong and others},
    journal={arXiv preprint arXiv:2306.06687},
    year={2023}
}

Assessment of Multimodal Large Language Models in Alignment with Human Values

@misc{shi2024assessment,
      title={Assessment of Multimodal Large Language Models in Alignment with Human Values}, 
      author={Zhelun Shi and Zhipin Wang and Hongxing Fan and Zaibin Zhang and Lijun Li and Yongting Zhang and Zhenfei Yin and Lu Sheng and Yu Qiao and Jing Shao},
      year={2024},
      eprint={2403.17830},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

ChEF

@misc{shi2023chef,
      title={ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models}, 
      author={Zhelun Shi and Zhipin Wang and Hongxing Fan and Zhenfei Yin and Lu Sheng and Yu Qiao and Jing Shao},
      year={2023},
      eprint={2311.02692},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Octavius

@misc{chen2023octavius,
      title={Octavius: Mitigating Task Interference in MLLMs via MoE}, 
      author={Zeren Chen and Ziqin Wang and Zhen Wang and Huayang Liu and Zhenfei Yin and Si Liu and Lu Sheng and Wanli Ouyang and Yu Qiao and Jing Shao},
      year={2023},
      eprint={2311.02684},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

DepictQA

@article{depictqa,
        title={Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models},
        author={You, Zhiyuan and Li, Zheyuan, and Gu, Jinjin, and Yin, Zhenfei and Xue, Tianfan and Dong, Chao},
        journal={arXiv preprint arXiv:2312.08962},
        year={2023}
    }

MP5

@misc{qin2023mp5,
  title         = {MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception}, 
  author        = {Yiran Qin and Enshen Zhou and Qichang Liu and Zhenfei Yin and Lu Sheng and Ruimao Zhang and Yu Qiao and Jing Shao},
  year          = {2023},
  eprint        = {2312.07472},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV}
}

Get Started

Please see tutorial for the basic usage of this repo.

License

The project is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.