ORPO_hh-seed3/README.md

base_model, datasets, library_name, model_name, tags, licence

base_model	datasets	library_name	model_name	tags	licence
Kyleyee/Qwen2.5-1.5B-sft-hh-3e	Kyleyee/train_data_Helpful_drdpo_preference	transformers	ORPO_hh-seed3	generated_from_trainer trl orpo	license

Model Card for ORPO_hh-seed3

This model is a fine-tuned version of Kyleyee/Qwen2.5-1.5B-sft-hh-3e on the Kyleyee/train_data_Helpful_drdpo_preference 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="Kyleyee/ORPO_hh-seed3", 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: 0.16.0.dev0
• Transformers: 4.49.0
• Pytorch: 2.6.0+cu126
• Datasets: 3.3.2
• Tokenizers: 0.21.0

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}}
}