nologin_-_ppo-gguf

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ppo - GGUF

• Model creator: https://huggingface.co/nologin/
• Original model: https://huggingface.co/nologin/ppo/

Original model description:

base_model: EleutherAI/pythia-160m datasets: trl-internal-testing/descriptiveness-sentiment-trl-style library_name: transformers model_name: ppo tags:

• generated_from_trainer licence: license

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Model Card for ppo

This model is a fine-tuned version of EleutherAI/pythia-160m 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="nologin/ppo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/> <img src="https://raw.githubusercontent.com/comet-ml/comet-examples/master/logo/comet_badge.png" alt="Visualize in Comet" width="135" height="20"/>

This model was trained with PPO, a method introduced in Fine-Tuning Language Models from Human Preferences.

Framework versions

• TRL: 0.13.0.dev0
• Transformers: 4.47.0
• 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}}
}