metadata

license: apache-2.0
datasets:
  - MathGenie/MathCode-Pile
language:
  - en
metrics:
  - accuracy
base_model:
  - codellama/CodeLlama-7b-hf
pipeline_tag: text-generation
tags:
  - math

QuantFactory/MathCoder2-CodeLlama-7B-GGUF

This is quantized version of MathGenie/MathCoder2-CodeLlama-7B created using llama.cpp

Original Model Card

MathCoder2

Introduction

The MathCoder2 models are created by conducting continued pretraining on MathCode-Pile. They are introduced in the paper MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code.

The mathematical pretraining dataset includes mathematical code accompanied with natural language reasoning steps, making it a superior resource for models aimed at performing advanced mathematical reasoning tasks.

Evaluation

Citation

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

@misc{lu2024mathcoder2bettermathreasoning,
      title={MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code}, 
      author={Zimu Lu and Aojun Zhou and Ke Wang and Houxing Ren and Weikang Shi and Junting Pan and Mingjie Zhan and Hongsheng Li},
      year={2024},
      eprint={2410.08196},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.08196}, 
}

@inproceedings{
wang2024mathcoder,
title={MathCoder: Seamless Code Integration in {LLM}s for Enhanced Mathematical Reasoning},
author={Zimu Lu and Aojun Zhou and Zimu Lu and Sichun Luo and Weikang Shi and Renrui Zhang and Linqi Song and Mingjie Zhan and Hongsheng Li},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=z8TW0ttBPp}
}