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Qwen2.5sft Grpo Rl Strategyqa Sft Strategy

This model is a fine-tuned version of unsloth/Qwen2.5-1.5B-Instruct. It has been trained using TRL.

KindLoRABase modelunsloth/Qwen2.5-1.5B-InstructVersionPublisher@Suryansh7123Cgrade

Model source

Kind
LoRA
File size
17.7 MB
Base model
unsloth/Qwen2.5-1.5B-Instruct
Version
vadapter_model
Updated
Oct 3, 2026
Publisher
@Suryansh7123
Downloads
0
Parameters
5B
Source
Hugging Face

Source description

Model Card for qwen2.5sft_grpo_rl_strategyqa_sft_strategy

This model is a fine-tuned version of unsloth/Qwen2.5-1.5B-Instruct. 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="Suryansh7123/qwen2.5sft_grpo_rl_strategyqa_sft_strategy", 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: 0.24.0
  • Transformers: 5.5.0
  • Pytorch: 2.10.0+cu128
  • Datasets: 4.3.0
  • 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:

@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}}
}

Sources

1 source
Hugging FacePrimaryVersion 0d22f2ab404c8e0bc43ae4a38bffa4f2c3571d64
huggingface.co/Suryansh7123/qwen2.5sft_grpo_rl_strategyqa_sft_strategy/tree/0d22f2ab404c8e0bc43ae4a38bffa4f2c3571d64

Verified Oct 3

Model artifacts

2 artifacts

adapter_model.safetensors

safetensors · 17.7 MB · SHA-256 6da026f26fd4…5bb1 · Hugging Face

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last-checkpoint/adapter_model.safetensors

safetensors · 17.7 MB · SHA-256 6da026f26fd4…5bb1 · Hugging Face

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Source excerpts

3 excerpts

--- base_model: unsloth/Qwen2.5-1.5B-Instruct library_name: transformers model_name: qwen2.5sft_grpo_rl_strategyqa_sft_strategy tags: - generated_from_trainer - unsloth - trl - grpo licence: license --- # Model Card for qwen2.5sft_grpo_rl_strategyqa_sft_strategy This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct). 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="Suryansh7123/qwen2.5sft_grpo_rl_strategyqa_sft_strategy", 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: 0.24.0 - Transformers: 5.5.0 - Pytorch: 2.10.0+cu128 - Datasets: 4.3.0 - 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 @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}} } ```

Source context: 0 downloads · 0 likes · Library transformers · Repo Suryansh7123/qwen2.5sft_grpo_rl_strategyqa_sft_strategy

Source context: 0 downloads · 0 likes · Library transformers · Repo Suryansh7123/qwen2.5sft_grpo_rl_strategyqa_sft_strategy