Update README.md

README.md

@@ -1,3 +1,58 @@
 ---
 license: mit
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-generation
 ---
+# MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
+
+Paper: [https://arxiv.org/pdf/2310.03731.pdf](https://arxiv.org/pdf/2310.03731.pdf)
+
+Repo: [https://github.com/mathllm/MathCoder](https://github.com/mathllm/MathCoder)
+
+
+## Introduction
+We introduce MathCoder, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving.
+
+| Base Model: Llama-2                                           	| Base Model: Code Llama                                                |
+|-------------------------------------------------------------------|-----------------------------------------------------------------------|
+|  [MathCoder-L-7B](https://huggingface.co/MathLLM/MathCoder-L-7B)  |  [MathCoder-CL-7B](https://huggingface.co/MathLLM/MathCoder-CL-7B)   	|
+|  [MathCoder-L-13B](https://huggingface.co/MathLLM/MathCoder-L-13B)  |  [MathCoder-CL-34B](https://huggingface.co/MathLLM/MathCoder-CL-34B)   	|
+
+
+## Training Data
+The models are trained on the [MathCodeInstruct](https://huggingface.co/datasets/MathLLM/MathCodeInstruct) Dataset.
+
+
+## Training Procedure
+The models are fine-tuned with the MathCodeInstruct dataset using the original Llama-2 and CodeLlama models as base models. Check out our paper and repo for more details.
+
+## Evaluation
+
+<br>
+<div align="center">
+  <img src="result.png" width="100%" title="Result Figure">
+</div>
+
+
+
+## Usage
+You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
+Check our Github repo for datails.
+
+
+## Citation
+Please cite the paper if you use our data, model or code.
+
+```
+@misc{wang2023mathcoder,
+      title={MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning}, 
+      author={Ke Wang and Houxing Ren and Aojun Zhou and Zimu Lu and Sichun Luo and Weikang Shi and Renrui Zhang and Linqi Song and Mingjie Zhan and Hongsheng Li},
+      year={2023},
+      eprint={2310.03731},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```