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OpenRS GRPO

This model is a fine-tuned version of Qwen/Qwen3.5-0.8B on the knoveleng/open-rs dataset. It has been trained using TRL.

KindLoRABase modelQwen/Qwen3.5-0.8BVersionadapter_modelPublisher@NTQuocCgrade

Model source

Kind
LoRA
File size
10.4 MB
Base model
Qwen/Qwen3.5-0.8B
Version
vadapter_model
Updated
Oct 2, 2026
Publisher
@NTQuoc
Downloads
6
Parameters
8B
Source
Hugging Face

Source description

Model Card for OpenRS-GRPO

This model is a fine-tuned version of Qwen/Qwen3.5-0.8B on the knoveleng/open-rs 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="NTQuoc/OpenRS-GRPO", 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: 0.16.0.dev0
  • Transformers: 5.8.0.dev0
  • Pytorch: 2.5.1
  • Datasets: 4.8.3
  • Tokenizers: 0.22.2

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 Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}

Sources

1 source
Hugging FacePrimaryVersion bf92dae9dbd76df167959a5ea6a7f48ac251a363
huggingface.co/NTQuoc/OpenRS-GRPO/tree/bf92dae9dbd76df167959a5ea6a7f48ac251a363

Verified Oct 2

Model artifacts

1 artifact

adapter_model.safetensors

safetensors · 10.4 MB · SHA-256 893f5b7a85ec…d3aa · Hugging Face

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Source excerpts

3 excerpts

--- base_model: Qwen/Qwen3.5-0.8B datasets: knoveleng/open-rs library_name: transformers model_name: OpenRS-GRPO tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for OpenRS-GRPO This model is a fine-tuned version of [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) on the [knoveleng/open-rs](https://huggingface.co/datasets/knoveleng/open-rs) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## 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="NTQuoc/OpenRS-GRPO", 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](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 5.8.0.dev0 - Pytorch: 2.5.1 - Datasets: 4.8.3 - Tokenizers: 0.22.2 ## Citations Cite GRPO as: ```bibtex @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: ```bibtex @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}} } ```

Source context: 6 downloads · 0 likes · Library transformers · Repo NTQuoc/OpenRS-GRPO

Source context: 0 downloads · 0 likes · Library transformers · Repo NTQuoc/OpenRS-GRPO