tergel
/

qwen2.5-3b-instruct-gsm8k-fs-gpt4o-bon

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+license: mit
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-3B-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/Qwen2.5-3B-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/qwen2.5-3b-instruct-gsm8k-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 = "A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?"
+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}
+}
+```