metadata
datasets: /weka/s225250685/mats-tist/data/hf_orpo_val_sel_paper_iter1_collab
library_name: transformers
tags:
- generated_from_trainer
- alignment-handbook
licence: license
Model Card for None
This model is a fine-tuned version of None on the /weka/s225250685/mats-tist/data/hf_orpo_val_sel_paper_iter1_collab 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="None", 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 ORPO, a method introduced in ORPO: Monolithic Preference Optimization without Reference Model.
Framework versions
- TRL: 0.13.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 4.8.5
- Tokenizers: 0.21.4
Citations
Cite ORPO as:
@article{hong2024orpo,
title = {{ORPO: Monolithic Preference Optimization without Reference Model}},
author = {Jiwoo Hong and Noah Lee and James Thorne},
year = 2024,
eprint = {arXiv:2403.07691}
}
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}}
}