metadata
base_model: Aratako/Llama-Gemma-2-27b-SimPO-trial3
library_name: transformers
model_name: fft-simpo3-iterative-iter1
tags:
- generated_from_trainer
- axolotl
- trl
- cpo
licence: license
Model Card for fft-simpo3-iterative-iter1
This model is a fine-tuned version of Aratako/Llama-Gemma-2-27b-SimPO-trial3. 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="Aratako/fft-simpo3-iterative-iter1", 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 CPO, a method introduced in Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation.
Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.3.1+cu121
- Datasets: 3.1.0
- Tokenizers: 0.20.3
Citations
Cite CPO as:
@inproceedings{xu2024contrastive,
title = {{Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}},
author = {Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
year = 2024,
booktitle = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
publisher = {OpenReview.net},
url = {https://openreview.net/forum?id=51iwkioZpn}
}
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}}
}