Qwen2-0.5B-GRPO-test2

Model Card for Qwen2-0.5B-GRPO-test2

This model is a fine-tuned version of Qwen/Qwen2-0.5B-Instruct on the AI-MO/NuminaMath-TIR dataset. It has been trained using TRL.

Quick start

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Hacoo1234/Qwen2-0.5B-GRPO-test2"
token = "paste code ere"  # your HF access token

trained_model = AutoModelForCausalLM.from_pretrained(
    model_id,
    use_auth_token=token,
    torch_dtype="auto",
    device_map="auto"
)

trained_tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    use_auth_token=token
)


## 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: 0.19.0
- Transformers: 4.53.0
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.2

## 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:

@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}}
}