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silviasapora/gemma-7b-silvia-basic-5e-5-05-v100

sourceHugging Faceupdated 1y agoView on Hugging Face

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Model Card

Model Card for google/gemma-7b

This model is a fine-tuned version of google/gemma-7b on the [['argilla/dpo-mix-7k']](https://huggingface.co/datasets/['argilla/dpo-mix-7k']) dataset. It has been trained using TRL.

Quick start

python

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="silviasapora/gemma-7b-silvia-basic-5e-5-05-v100", 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: 0.15.2
—Transformers: 4.49.0
—Pytorch: 2.4.0
—Datasets: 3.0.0
—Tokenizers: 0.21.0

Citations

Cite ORPO as:

bibtex

@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:

bibtex

@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}}
}