Exllamav2 quant (exl2 / 3.75 bpw) made with ExLlamaV2 v0.1.1
Other EXL2 quants:
| Quant | Model Size | lm_head |
|---|---|---|
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
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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