vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-NashMDPG-0401225210-epoch-9

Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-NashMDPG

This model is a fine-tuned version of vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT on the PKU-Alignment/PKU-SafeRLHF dataset. 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="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-NashMDPG-0401225210-epoch-9", 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 Nash-MD, a method introduced in Nash Learning from Human Feedback.

Framework versions

• TRL: 0.13.0
• Transformers: 4.48.0
• Pytorch: 2.2.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citations

Cite Nash-MD as:

@inproceedings{munos2024nash,
    title        = {Nash Learning from Human Feedback},
    author       = {R{'{e}}mi Munos and Michal Valko and Daniele Calandriello and Mohammad Gheshlaghi Azar and Mark Rowland and Zhaohan Daniel Guo and Yunhao Tang and Matthieu Geist and Thomas Mesnard and C{\^{o}}me Fiegel and Andrea Michi and Marco Selvi and Sertan Girgin and Nikola Momchev and Olivier Bachem and Daniel J. Mankowitz and Doina Precup and Bilal Piot},
    year         = 2024,
    booktitle    = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
    publisher    = {OpenReview.net},
    url          = {https://openreview.net/forum?id=Y5AmNYiyCQ}
}

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 Gallouédec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}