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README

Quick start

python

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="VikramR/cypherbench-grpo-4.3", 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: 1.6.0
  • Transformers: 5.12.0
  • Pytorch: 2.10.0+cu129
  • Datasets: 4.8.5
  • Tokenizers: 0.22.2

Citations

Cite GRPO as:

bibtex

@article{shao2024deepseekmath,
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:

bibtex

@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}
}

Model provider

VikramR

VikramR

Model tree

Base

google/gemma-4-E2B-it

Fine-tuned

this model

Modalities

Input

Text, Image

Output

Text

Pricing

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Supported Functionality

Model APIs

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Container

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