Model Card for Mistral-7B-v0.1-Math-Shepherd-PRM-token-0.1
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 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/Mistral-7B-v0.1-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}}
}
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Model tree for plaguss/Mistral-7B-v0.1-Math-Shepherd-PRM-token-0.1
Base model
mistralai/Mistral-7B-v0.1