metadata

base_model: Qwen/Qwen2.5-0.5B
datasets: plaguss/math_shepherd_token
library_name: transformers
model_name: Qwen2.5-0.5B-Math-Shepherd-PRM-token-0.1
tags:
  - generated_from_trainer
  - trl
  - stepwise-reward-trainer
licence: license

Model Card for Qwen2.5-0.5B-Math-Shepherd-PRM-token-0.1

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B on the plaguss/math_shepherd_token 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="plaguss/Qwen2.5-0.5B-Math-Shepherd-PRM-token-0.1", 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 Stepwise Reward.

Framework versions

TRL: 0.13.0.dev0
Transformers: 4.46.0.dev0
Pytorch: 2.4.1
Datasets: 3.0.1
Tokenizers: 0.20.1

Citations

Cite Stepwise Reward as:

@article{uesato2022solving,
    title        = {Solving Math Word Problems With Process- and Outcome-Based Feedback},
    author       = {Uesato, Jonathan and Kushman, Nate and Kumar, Ramana and Song, Francis and Siegel, Noah and Wang, Lisa and Creswell, Antonia and Irving, Geoffrey and Higgins, Irina},
    year         = 2022,
    journal      = {arXiv preprint arXiv:2211.14275}
}

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}}
}