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jordanpainter/diallm-gemma-dpo-ind
diallm-gemma-dpo-ind is a image-text-to-text model from jordanpainter. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as gemma.
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From the Hugging Face model README
Built with Gemma.
Part of DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation (EMNLP 2026 Main).

Fine-tuned from jordanpainter/diallm-gemma-sft-ind via DPO with the target-variety preference pairs.
Code, checkpoints, preference datasets, linguistic-analysis toolkit: https://github.com/surrey-nlp/diallm
Paper: https://arxiv.org/abs/2607.07669
This model is a fine-tuned version of jordanpainter/diallm-gemma-sft-ind, trained using TRL.
This model was trained with DPO.
@article{painter2026diallm,
title = {DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation},
author = {Painter, Jordan and Srirag, Dipankar and Kappiyath, Adarsh and Kanojia, Diptesh and Joshi, Aditya and Yin, Lu},
year = {2026},
eprint = {2607.07669},
archivePrefix = {arXiv}
}
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}
}
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}