Model Card for dpo_synthetic

This model is a fine-tuned version of bbunzeck/llamalogue. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fpadovani/communicative-baby-dpo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with DPO, a method introduced in Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Framework versions

TRL: 0.19.1
Transformers: 4.53.2
Pytorch: 2.7.1
Datasets: 4.0.0
Tokenizers: 0.21.2

Citations

For further information please consult the accompanying paper. If you make use of this model in your work, please also cite the paper:

@inproceedings{padovani-etal-2025-dialogue,
    title = "Dialogue Is Not Enough to Make a Communicative {B}aby{LM} (But Neither Is Developmentally Inspired Reinforcement Learning)",
    author = "Padovani, Francesca  and Bunzeck, Bastian  and Ali, Manar  and Momen, Omar  and Bisazza, Arianna  and Buschmeier, Hendrik  and Zarrie{\ss}, Sina",
    editor = "Charpentier, Lucas  and Choshen, Leshem  and Cotterell, Ryan  and Gul, Mustafa Omer  and Hu, Michael Y.  and Liu, Jing  and Jumelet, Jaap  and Linzen, Tal  and Mueller, Aaron  and Ross, Candace  and Shah, Raj Sanjay  and Warstadt, Alex  and Wilcox, Ethan Gotlieb  and Williams, Adina",
    booktitle = "Proceedings of the First BabyLM Workshop",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.babylm-main.29/",
    pages = "421--435",
}

Cite DPO as:

@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: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}