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="leobianco/npov_PERL_google_S130104_epo0.2_lr1.1e-05_beta0.078_2608141455", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

Visualize in Weights & Biases

This model was trained with RLOO, a method introduced in Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs.

Framework versions

TRL: 1.9.2
Transformers: 5.14.1
Pytorch: 2.11.0
Datasets: 5.0.1
Tokenizers: 0.22.2

Citations

Cite RLOO as:

bibtex

@inproceedings{ahmadian2024back,
    title        = {{Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs}},
    author       = {Arash Ahmadian and Chris Cremer and Matthias Gall{'{e}} and Marzieh Fadaee and Julia Kreutzer and Olivier Pietquin and Ahmet {"{U}}st{"{u}}n and Sara Hooker},
    year         = 2024,
    booktitle    = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), {ACL} 2024, Bangkok, Thailand, August 11-16, 2024},
    pages        = {12248--12267},
    publisher    = {Association for Computational Linguistics},
    editor       = {Lun{-}Wei Ku and Andre Martins and Vivek Srikumar},
}

Cite TRL as:

bibtex

@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}
}