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Exllamav2 quant (exl2 / 3.75 bpw) made with ExLlamaV2 v0.1.1

Other EXL2 quants:

Quant Model Size lm_head
2.2
2055 MB
6
2.5
2276 MB
6
3.0
2665 MB
6
3.5
3051 MB
6
3.75
3245 MB
6
4.0
3437 MB
6
4.25
3630 MB
6
5.0
4208 MB
6
6.0
5000 MB
8
6.5
5388 MB
8
8.0
6232 MB
8

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

📄 Paper🏠 Repo🤖 Models📚 Datasets

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!


Models

Model Checkpoint Size HumanEval (+) MBPP (+) License
ReflectionCoder-CL-7B 🤗 HF Link 7B 75.0 (68.9) 72.2 (61.4) Llama2
ReflectionCoder-CL-34B 🤗 HF Link 34B 70.7 (66.5) 68.4 (56.6) Llama2
ReflectionCoder-DS-6.7B 🤗 HF Link 6.7B 80.5 (74.4) 81.5 (69.6) DeepSeek
ReflectionCoder-DS-33B 🤗 HF Link 33B 82.9 (76.8) 84.1 (72.0) DeepSeek

Datasets

Dataset Link License
ReflectionSeq-GPT 🤗 HF Link License
ReflectionSeq-DS 🤗 HF Link License

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:

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Datasets used to train Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-3_75bpw_exl2