metadata
base_model: google/gemma-4-E4B-it
library_name: transformers
model_name: trainer_output
tags:
- generated_from_trainer
- rloo
- trl
licence: license
Model Card for trainer_output
This model is a fine-tuned version of google/gemma-4-E4B-it. 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="leobianco/trainer_output", 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 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:
@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:
@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}
}