Model Card for out_steponly_math_qwen2.5_rlhflow_mistral_7B

This model is a fine-tuned version of Qwen/Qwen2.5-Math-7B-Instruct. 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="None", 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 BidirectionalPRM, a method introduced in Solving math word problems with process-and outcome-based feedback.

Framework versions

• TRL: 0.29.0
• Transformers: 5.12.1
• Pytorch: 2.12.1
• Datasets: 5.0.0
• Tokenizers: 0.22.2

Citations

Cite BidirectionalPRM 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:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}