65 lines
2.3 KiB
Markdown
65 lines
2.3 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- MathGenie/MathCode-Pile
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language:
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- en
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metrics:
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- accuracy
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base_model:
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- meta-llama/Meta-Llama-3-8B
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pipeline_tag: text-generation
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tags:
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- math
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---
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[](https://hf.co/QuantFactory)
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# QuantFactory/MathCoder2-Llama-3-8B-GGUF
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This is quantized version of [MathGenie/MathCoder2-Llama-3-8B](https://huggingface.co/MathGenie/MathCoder2-Llama-3-8B) created using llama.cpp
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# Original Model Card
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# MathCoder2
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### Introduction
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The MathCoder2 models are created by conducting continued pretraining on [MathCode-Pile](https://huggingface.co/datasets/MathGenie/MathCode-Pile). They are introduced in the paper [MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code](https://arxiv.org/abs/2410.08196).
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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.
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### Evaluation
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### Citation
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If you find this repository helpful, please consider citing our papers:
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```
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@misc{lu2024mathcoder2bettermathreasoning,
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title={MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code},
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author={Zimu Lu and Aojun Zhou and Ke Wang and Houxing Ren and Weikang Shi and Junting Pan and Mingjie Zhan and Hongsheng Li},
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year={2024},
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eprint={2410.08196},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2410.08196},
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}
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```
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```
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@inproceedings{
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wang2024mathcoder,
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title={MathCoder: Seamless Code Integration in {LLM}s for Enhanced Mathematical Reasoning},
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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},
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booktitle={The Twelfth International Conference on Learning Representations},
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year={2024},
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url={https://openreview.net/forum?id=z8TW0ttBPp}
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}
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```
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