61 lines
2.1 KiB
Markdown
61 lines
2.1 KiB
Markdown
---
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base_model:
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- Qwen/Qwen2.5-Math-1.5B-Instruct
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language:
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- en
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library_name: transformers
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license: mit
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pipeline_tag: text-generation
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---
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# Self-Training Elicits Concise Reasoning in Large Language Models
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This model is fine-tuned using self-training methods to generate concise reasoning paths for reasoning tasks while maintaining accuracy.
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## Model Details
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- **Developed by:** Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim, Yongjin Yang, Yujin Kim, Se-Young Yun at KAIST AI
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- **Model type:** Fine-tuned Large Language Model for concise reasoning
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- **Language(s) (NLP):** English
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- **License:** MIT
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- **Finetuned from model:** Qwen/Qwen2.5-Math-1.5B-Instruct
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- **Repository:** https://github.com/TergelMunkhbat/concise-reasoning
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- **Paper:** [Self-Training Elicits Concise Reasoning in Large Language Models](https://huggingface.co/papers/2502.20122)
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_name = "tergel/qwen2.5-math-1.5b-instruct-gsm8k-fs-gpt4o-bon"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map=device, torch_dtype=torch.bfloat16)
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question = "A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?"
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inputs = tokenizer(question, return_tensors="pt").to(device)
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input_length = len(inputs['input_ids'][0])
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outputs = model.generate(**inputs, max_new_tokens=512)
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response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)
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print(response)
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```
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For more detailed information about training methods, evaluation results, limitations, and technical specifications, please refer to our [paper](https://huggingface.co/papers/2502.20122).
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## Citation
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```
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@article{munkhbat2025self,
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title={Self-Training Elicits Concise Reasoning in Large Language Models},
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author={Munkhbat, Tergel and Ho, Namgyu and Kim, Seohyun and Yang, Yongjin and Kim, Yujin and Yun, Se-Young},
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journal={arXiv preprint arXiv:2502.20122},
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year={2025}
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}
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``` |