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ReflectionCoder-CL-34B - GGUF

Name Quant method Size
ReflectionCoder-CL-34B.Q2_K.gguf Q2_K 11.65GB
ReflectionCoder-CL-34B.Q3_K_S.gguf Q3_K_S 13.6GB
ReflectionCoder-CL-34B.Q3_K.gguf Q3_K 15.19GB
ReflectionCoder-CL-34B.Q3_K_M.gguf Q3_K_M 15.19GB
ReflectionCoder-CL-34B.Q3_K_L.gguf Q3_K_L 16.55GB
ReflectionCoder-CL-34B.IQ4_XS.gguf IQ4_XS 16.99GB
ReflectionCoder-CL-34B.Q4_0.gguf Q4_0 17.74GB
ReflectionCoder-CL-34B.IQ4_NL.gguf IQ4_NL 17.92GB
ReflectionCoder-CL-34B.Q4_K_S.gguf Q4_K_S 17.87GB
ReflectionCoder-CL-34B.Q4_K.gguf Q4_K 18.83GB
ReflectionCoder-CL-34B.Q4_K_M.gguf Q4_K_M 18.83GB
ReflectionCoder-CL-34B.Q4_1.gguf Q4_1 19.69GB
ReflectionCoder-CL-34B.Q5_0.gguf Q5_0 21.64GB
ReflectionCoder-CL-34B.Q5_K_S.gguf Q5_K_S 21.64GB
ReflectionCoder-CL-34B.Q5_K.gguf Q5_K 22.2GB
ReflectionCoder-CL-34B.Q5_K_M.gguf Q5_K_M 22.2GB
ReflectionCoder-CL-34B.Q5_1.gguf Q5_1 23.59GB
ReflectionCoder-CL-34B.Q6_K.gguf Q6_K 25.78GB
ReflectionCoder-CL-34B.Q8_0.gguf Q8_0 33.39GB

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

📄 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.

import torch
from transformers import pipeline

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

generator = pipeline(
    model="SenseLLM/ReflectionCoder-CL-34B",
    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: