SenseLLM_ReflectionCoder-DS-6.7B-3_0bpw_exl2

Exllamav2 quant (exl2 / 3.0 bpw) made with ExLlamaV2 v0.1.1

Other EXL2 quants:

ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation

<p align="center"> <a href="https://arxiv.org/abs/2405.17057">📄 Paper</a> • <a href="https://github.com/SenseLLM/ReflectionCoder">🏠 Repo</a> • <a href="https://huggingface.co/SenseLLM/ReflectionCoder-DS-33B">🤖 Models</a> • <a href="https://huggingface.co/datasets/SenseLLM/ReflectionSeq-GPT">📚 Datasets </a> </p>

Introduction

ReflectionCoder is a novel approach that effectively leverages reflection sequences constructed by integrating compiler feedback to improve one-off code generation performance. Please refer to our paper and repo for more details!

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Models

Datasets

How to Use

Chat Format

Following chat templates of most models, we use two special tokens to wrap the message of user and assistant, i.e., <|user|>, <|assistant|>, and <|endofmessage|>. Furthermore, we use two special tokens to wrap the content of different blocks, i.e., <|text|> and <|endofblock|>. You can use the following template to prompt our ReflectionCoder.

<|user|><|text|> 
Your Instruction
<|endofblock|><|endofmessage|><|assistant|>

Inference Code

Please refer to our GitHub Repo for more technical details.

Citation

If you find this repo useful for your research, please kindly cite our paper:

@misc{ren2024reflectioncoder,
    title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation}, 
    author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
    year={2024},
    eprint={2405.17057},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Acknowledgments

We thank the following amazing projects that truly inspired us:

• CodeLlama
• DeepSeek-Coder
• WizardCoder
• Evol-CodeAlpaca-v1
• MagiCoder
• EvalPlus
• OpenCoderInterpreter