rho-math-1b-v0.1

🔥 News

• [2024/04/12] 🔥🔥🔥 Rho-Math-v0.1 models released at 🤗 HuggingFace!
  • Rho-Math-1B and Rho-Math-7B achieve 15.6% and 31.0% few-shot accuracy on MATH dataset, respectively — matching DeepSeekMath with only 3% of the pretraining tokens.
  • Rho-Math-1B-Interpreter is the first 1B LLM that achieves over 40% accuracy on MATH.
  • Rho-Math-7B-Interpreter achieves 52% on MATH dataset, using only 69k samples for fine-tuning.
• [2024/04/11] Rho-1 paper and repo released.

💡 Introduction

Rho-1 base models employ Selective Language Modeling (SLM) for pretraining, which selectively trains on clean and useful tokens that aligned with the desired distribution.

Selective Lanugage Modeling (SLM)

Evaluation Results

Base models (Few-shot CoT):

Tool-integrated reasoning (Code Interpreter):

🚀 Quick Start

Evaluation

git clone git@github.com:microsoft/rho.git
cd rho-1/math-evaluation-harness

Base model few-shot evaluation:

bash scripts/run_eval.sh cot microsoft/rho-math-7b-v0.1

SFT model (code-interpreter) evaluation:

bash scripts/run_eval.sh tora microsoft/rho-math-7b-interpreter-v0.1

Our reproduced outputs are provided in rho-1/outputs.zip.

☕️ Citation

If you find this repository helpful, please consider citing our paper:

@misc{lin2024rho1,
      title={Rho-1: Not All Tokens Are What You Need}, 
      author={Zhenghao Lin and Zhibin Gou and Yeyun Gong and Xiao Liu and Yelong Shen and Ruochen Xu and Chen Lin and Yujiu Yang and Jian Jiao and Nan Duan and Weizhu Chen},
      year={2024},
      eprint={2404.07965},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}