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zeliang0426 / bug-fix-GSM8K-CacheTraining-Adapter
Model's Last Updated: July 28 2025
text-generation
Introduction of bug-fix-GSM8K-CacheTraining-Adapter
Model Details of bug-fix-GSM8K-CacheTraining-Adapter
Model Card for bug-fix-GSM8K-CacheTraining-Adapter
This model is a fine-tuned version of None . 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="zeliang0426/bug-fix-GSM8K-CacheTraining-Adapter", 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 GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models .
Framework versions
- TRL: 0.20.0.dev0
- Transformers: 4.53.3
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.2
Citations
Cite GRPO as:
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
Runs of zeliang0426 bug-fix-GSM8K-CacheTraining-Adapter on huggingface.co
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More Information About bug-fix-GSM8K-CacheTraining-Adapter huggingface.co Model
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