metadata
base_model: google/gemma-2-2b
library_name: transformers
model_name: GEMMA-2-2B-FT-ORPO-ISAERFT_gemma-2-2b-lr4.4e-06-beta0.05-20250301-0334
tags:
- generated_from_trainer
- smol-course
- module_1
- isaerft
- lr_4.357312202652237e-06
- beta_0.05
licence: license
Model Card for GEMMA-2-2B-FT-ORPO-ISAERFT_gemma-2-2b-lr4.4e-06-beta0.05-20250301-0334
This model is a fine-tuned version of google/gemma-2-2b. 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="AMindToThink/GEMMA-2-2B-FT-ORPO-ISAERFT_gemma-2-2b-lr4.4e-06-beta0.05-20250301-0334", 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: 0.15.1
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 2.21.0
- 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}}
}