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---
license: apache-2.0
datasets:
- SenseLLM/ReflectionSeq-GPT
- SenseLLM/ReflectionSeq-DS
language:
- en
---
**Exllamav2** quant (**exl2** / **2.5 bpw**) made with ExLlamaV2 v0.1.1
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-2_2bpw_exl2)**</center> | <center>2055 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-2_5bpw_exl2)**</center> | <center>2276 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-3_0bpw_exl2)**</center> | <center>2665 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-3_5bpw_exl2)**</center> | <center>3051 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-3_75bpw_exl2)**</center> | <center>3245 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-4_0bpw_exl2)**</center> | <center>3437 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-4_25bpw_exl2)**</center> | <center>3630 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-5_0bpw_exl2)**</center> | <center>4208 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-6_0bpw_exl2)**</center> | <center>5000 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-6_5bpw_exl2)**</center> | <center>5388 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co./Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-8_0bpw_exl2)**</center> | <center>6232 MB</center> | <center>8</center> |
## 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!
<hr>
## Models
| Model | Checkpoint | Size | HumanEval (+) | MBPP (+) | License|
|:-------|:------------|:------|:---------------|:----------|:--------|
| ReflectionCoder-CL-7B | 🤗 [HF Link](https://huggingface.co./SenseLLM/ReflectionCoder-CL-7B) | 7B | 75.0 (68.9) | 72.2 (61.4) | [Llama2](https://ai.meta.com/llama/license/) |
| ReflectionCoder-CL-34B | 🤗 [HF Link](https://huggingface.co./SenseLLM/ReflectionCoder-CL-34B) | 34B | 70.7 (66.5) | 68.4 (56.6) | [Llama2](https://ai.meta.com/llama/license/) |
| ReflectionCoder-DS-6.7B | 🤗 [HF Link](https://huggingface.co./SenseLLM/ReflectionCoder-DS-6.7B) | 6.7B | 80.5 (74.4) | 81.5 (69.6) | [DeepSeek](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL) |
| ReflectionCoder-DS-33B | 🤗 [HF Link](https://huggingface.co./SenseLLM/ReflectionCoder-DS-33B) | 33B | 82.9 (76.8) | 84.1 (72.0) | [DeepSeek](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL) |
## Datasets
| Dataset | Link | License |
|:-------------------|:----------------|:----------------------------------------------|
| ReflectionSeq-GPT | 🤗 [HF Link](https://huggingface.co./datasets/SenseLLM/ReflectionSeq-GPT) | [License](LICENSE) |
| ReflectionSeq-DS | 🤗 [HF Link](https://huggingface.co./datasets/SenseLLM/ReflectionSeq-DS) | [License](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.
```python
<|user|><|text|>
Your Instruction
<|endofblock|><|endofmessage|><|assistant|>
```
#### Inference Code
Please refer to our [GitHub Repo](https://github.com/SenseLLM/ReflectionCoder) 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](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)
- [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder)
- [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder)
- [Evol-CodeAlpaca-v1](https://huggingface.co./datasets/theblackcat102/evol-codealpaca-v1)
- [MagiCoder](https://github.com/ise-uiuc/magicoder/tree/main)
- [EvalPlus](https://github.com/evalplus/evalplus)
- [OpenCoderInterpreter](https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/tree/main) |