metadata
base_model: Qwen/Qwen2.5-Math-1.5B-Instruct
library_name: transformers
model_name: out_biprm_math_qwen2.5_rlhflow_mistral
tags:
- generated_from_trainer
- bidirectional-prm
- trl
licence: license
Model Card for out_biprm_math_qwen2.5_rlhflow_mistral
This model is a fine-tuned version of Qwen/Qwen2.5-Math-1.5B-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}
}