README.md

---
library_name: transformers
license: mit
language:
- en
base_model:
- Qwen/Qwen2.5-Math-1.5B-Instruct
pipeline_tag: text-generation
---

# Self-Training Elicits Concise Reasoning in Large Language Models

This model is fine-tuned using self-training methods to generate concise reasoning paths for reasoning tasks while maintaining accuracy.



## Model Details

- **Developed by:** Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim, Yongjin Yang, Yujin Kim, Se-Young Yun at KAIST AI
- **Model type:** Fine-tuned Large Language Model for concise reasoning
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** Qwen/Qwen2.5-Math-1.5B-Instruct
- **Repository:** https://github.com/TergelMunkhbat/concise-reasoning
- **Paper:** [Self-Training Elicits Concise Reasoning in Large Language Models](https://arxiv.org/abs/2502.20122)

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "tergel/qwen2.5-math-1.5b-instruct-math-fs-gpt4o-bon"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map=device, torch_dtype=torch.bfloat16)

question = "Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$"

inputs = tokenizer(question, return_tensors="pt").to(device)
input_length = len(inputs['input_ids'][0])

outputs = model.generate(**inputs, max_new_tokens=512)

response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)
print(response)
```

For more detailed information about training methods, evaluation results, limitations, and technical specifications, please refer to our [paper](https://arxiv.org/abs/2502.20122).

## Citation

```
@article{munkhbat2025self,
  title={Self-Training Elicits Concise Reasoning in Large Language Models},
  author={Munkhbat, Tergel and Ho, Namgyu and Kim, Seohyun and Yang, Yongjin and Kim, Yujin and Yun, Se-Young},
  journal={arXiv preprint arXiv:2502.20122},
  year={2025}
}
```