Table of Links
4 Human Evaluations and Safety Testing, and 4.1 Prompts for Evaluation
5 Benchmark Evaluations and 5.1 Text
7 Conclusion, Acknowledgements, Contributors, and References
Appendix
B. Additional Information of Human Evaluations
7 Conclusion
In this paper, we introduced Chameleon, a new family of early-fusion token-based foundation models that set a new bar for multimodal machine learning. By learning a unified representation space over interleaved image and text tokens, Chameleon is a single model that achieves strong performance across a wide range of vision-language benchmarks while enabling new mixed-modal reasoning and generation capabilities.
The key to Chameleon’s success is its fully token-based architecture, which allows for seamless information integration across modalities. By quantizing images into discrete tokens and training on mixed-modal data from scratch, Chameleon learns to jointly reason over image and text in a way that is impossible with late-fusion architectures or models that maintain separate encoders for each modality. At the same time, Chameleon introduces novel techniques for stable and scalable training of early-fusion models, addressing key optimization and architectural design challenges that have previously limited the scale of such approaches. On tasks such as image captioning and visual question answering, Chameleon-34B outperforms models such as Flamingo and IDEFICS, while maintaining competitive performance on text-only benchmarks. Chameleon also unlocks entirely new possibilities for multimodal interaction, as demonstrated by its strong performance on our new benchmark for mixed-modal open-ended QA.
Acknowledgements
We thank Naren Briar for her invaluable contribution to manually curating safety prompts, which were crucial for our safety tuning efforts. We also thank Pierre Fernandez for his indispensable support with the Chameleon release, Shelly Sheynin for her work on the Chameleon image tokenizer, Puxin Xu and David for helping us with datasets. Additionally, we thank Mitchell Wortsman for engaging in insightful discussions about stability in large-scale language models and Mike Lewis for general discussions and advice throughout the project. We thank Aaron Grattafiori, Firat Ozgenel, Divya Shah, Danny Livshits, Cristian Canton Ferrer, Saghar Hosseini, Ramon Calderer, Joshua Saxe, Daniel Song and Manish Bhatt for their help with the safety and red teaming efforts.
Contributors
We attribute credit separated by bucket of work. Additionally, ∗ indicates joint first authors, † indicates key contributors, ‡ indicates workstream leads, and ♯ indicates project leads.
Pre-Training: Srinivasan Iyer∗ , Bernie Huang∗ , Lili Yu† , Arun Babu† , Chunting Zhou† , Kushal Tirumala, Xi Victoria Lin, Hu Xu, Xian Li, Akshat Shrivastava, Omer Levy‡ , Armen Aghajanyan∗‡
Alignment and Safety: Ram Pasunuru∗ , Andrew Cohen† , Aram H. Markosyan† , Koustuv Sinha† , Xiaoqing Ellen Tan† , Ivan Evtimov, Ping Yu, Tianlu Wang, Olga Golovneva, Asli Celikyilmaz‡
Inference and Evaluation: Pedro Rodriguez† , Leonid Shamis† , Vasu Sharma† , Christine Jou, Karthik Padthe† , Ching-Feng Yeh, Mingda Chen, Bapi Akula, Jacob Kahn‡ , Daniel Li‡ , Scott Yih‡
Overall Project: Barlas Oguz, Morteza Behrooz, Benjamin Muller, Carleigh Wood, Mary Williamson, Ramya Raghavendra, Barbara Usher, William Ngan, Nikolay Bashlykov, Lukas Blecher, Sony Theakanath (Lead PM), Ammar Rizvi (Lead TPM), Gargi Ghosh♯ , Luke Zettlemoyer♯
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A Samples
B Additional Information of Human Evaluations
For the twelve task categories of the prompts we collected for human evaluation, a short description of each category can be found in Table 8.
The task fulfillment rates, broken down by each task category and modality are shown in Table 9 and Table 10.
Chameleon’s win rates, broken down by task category and modality, are shown in Table 11, Table 12, Table 13 and Table 14.
Author:
(1) Chameleon Team, FAIR at Meta.
This paper is
