README.md

---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Thinking-v1.1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
datasets:
- microsoft/orca-math-word-problems-200k
model-index:
   - name: Qwen2.5-1.5B-Thinking-v1.1
     results:
       - task:
           type: text-generation
         dataset:
           name: openai/gsm8k
           type: GradeSchoolMath8K
         metrics:
           - name: GSM8k (0-Shot)
             type: GSM8k (0-Shot)
             value: 17%
           - name: GSM8k (Few-Shot)
             type: GSM8k (Few-Shot)
             value: 64.2%
co2_eq_emissions:
  emissions: 7100
  source: "https://mlco2.github.io/impact#compute"
  training_type: "GRPO"
  geographical_location: "East US2"
  hardware_used: "1 x H100 96GB"

---

# Model Card for Qwen2.5-1.5B-Thinking-v1.1

This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).


## Evals

| Model                                    | GSM8k 0-Shot | GSM8k Few-Shot |
|------------------------------------------|------------------|-------------------|
| Mistral-7B-v0.1                          | 10%             | 41%              |
| Qwen2.5-1.5B-Thinking             | 17%            | 64.2%                |

## Training procedure

<img src="https://raw.githubusercontent.com/wandb/wandb/fc186783c86c33980e5c73f13363c13b2c5508b1/assets/logo-dark.svg" alt="Weights & Biases Logged" width="150" height="24"/>

<img src="https://huggingface.co/justinj92/Qwen2.5-1.5B-Thinking-v1.1/resolve/main/wandb_v1.1.png" width="1200" height="1200"/>

Trained on 1xH100 96GB via Azure Cloud (East US2).

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).

### Usage Recommendations

**Recommend adhering to the following configurations when utilizing the models, including benchmarking, to achieve the expected performance:**

1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
2. **For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."**
3. When evaluating model performance, it is recommended to conduct multiple tests and average the results.
4. This model is not enhanced for other domains apart from Maths.

### Framework versions

- TRL: 0.15.0.dev0
- Transformers: 4.49.0.dev0
- Pytorch: 2.5.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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}}
}
```