metadata
base_model: mistralai/Ministral-3-3B-Instruct-2512-BF16
library_name: peft
model_name: mistral-grpo
tags:
- base_model:adapter:mistralai/Ministral-3-3B-Instruct-2512-BF16
- grpo
- lora
- transformers
- trl
licence: license
pipeline_tag: text-generation
Model Card for mistral-grpo
This model is a fine-tuned version of mistralai/Ministral-3-3B-Instruct-2512-BF16. 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 GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Framework versions
- PEFT 0.18.1
- TRL: 0.29.0
- Transformers: 5.2.0
- Pytorch: 2.6.0+cu124
- Datasets: 4.6.1
- Tokenizers: 0.22.2
Citations
Cite GRPO as:
@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:
@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}
}