SenseLLM_-_ReflectionCoder-DS-6.7B-gguf

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ReflectionCoder-DS-6.7B - GGUF

• Model creator: https://huggingface.co/SenseLLM/
• Original model: https://huggingface.co/SenseLLM/ReflectionCoder-DS-6.7B/

Original model description:

license: apache-2.0 datasets:

• SenseLLM/ReflectionSeq-GPT
• SenseLLM/ReflectionSeq-DS language:
• en

---

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

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.

import torch
from transformers import pipeline

chat = [
    {"role": "user", "content": "<Your code instruction here>"}
]

generator = pipeline(
    model="SenseLLM/ReflectionCoder-DS-6.7B",
    task="text-generation",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

result = generator(chat, max_length=128, num_return_sequences=1)

print(result)

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