metadata

base_model: EleutherAI/pythia-70m-deduped
datasets: trl-internal-testing/descriptiveness-sentiment-trl-style
library_name: transformers
model_name: FedPPO-Collaborative-Pythia-70M-test-a1
tags:
  - generated_from_trainer
licence: license

Model Card for FedPPO-Collaborative-Pythia-70M-test-a1

This model is a fine-tuned version of EleutherAI/pythia-70m-deduped on the trl-internal-testing/descriptiveness-sentiment-trl-style 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="RLHF-And-Friends/FedPPO-Collaborative-Pythia-70M-test-a1", 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 PPO, a method introduced in Fine-Tuning Language Models from Human Preferences.

Framework versions

TRL: 0.13.0
Transformers: 4.47.1
Pytorch: 2.5.1
Datasets: 3.2.0
Tokenizers: 0.21.0

Citations

Cite PPO as:

@article{mziegler2019fine-tuning,
    title        = {{Fine-Tuning Language Models from Human Preferences}},
    author       = {Daniel M. Ziegler and Nisan Stiennon and Jeffrey Wu and Tom B. Brown and Alec Radford and Dario Amodei and Paul F. Christiano and Geoffrey Irving},
    year         = 2019,
    eprint       = {arXiv:1909.08593}
}

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}}
}