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Fine-tuning

We only provide the code to fuse instructions. You can use the following GitHub repo for fine-tuning Code Llama: https://github.com/hiyouga/LLaMA-Factory/tree/main

Dataset

download it from huggingface:

Evaluation

Use evalplus to evaluate on python benchmarks: https://github.com/evalplus/evalplus

Use bigcode-evaluation-harness to evalute the performance on MultiPL-E https://github.com/bigcode-project/bigcode-evaluation-harness

Seed Template

{ "dataset": "your dataset name", "id": 0, "instruction": "", }

We use evol-codealpaca-v1 as seeds.

Performance

Method Size Open-source HumanEval HumanEval+ MBPP MBPP+
gpt-4-1106-preview - - 85.4 81.7 83.0 70.7
gpt-3.5-turbo-1106 - - 72.6 65.9 81.7 69.4
StarCoder 7B weight&data 24.4 20.7 33.1 28.8
Mistral 7B weight 28.7 23.2 50.1 40.9
CodeLlama-Python 7B weight 37.8 34.1 57.6 45.4
WizardCoder-CLP 7B weight 48.2 40.9 56.6 47.1
MagicoderS-CLP 7B weight&data 70.7 66.5 68.4 56.6
CodeLlama-Python 13B weight 42.7 36.6 61.2 50.9
StarCoder 15B weight&data 34.1 29.3 55.1 46.1
CodeT5+ 16B weight&data 31.7 26.2 54.6 44.4
CodeGen-Mono 16B weight&data 32.9 27.4 52.6 43.6
CodeLlama-Python 34B weight 51.8 42.7 67.2 52.9
WizardCoder-CLP 34B weight 73.2 64.6 73.2 59.9
IF-CLP 13B weight&data 73.8 69.5 71.7 61.7
IF-CL 13B weight&data 74.4 68.3 69.7 59.4
IF-CLP 34B weight&data 75.6 69.5 73.7 62.7
IF-CL 34B weight&data 78.7 71.3 71.4 60.7
IF-CL-MC 7B weight&data 76.2 71.3 70.4 57.9
IF-CL-MC 13B weight&data 79.3 72.6 69.2 57.4
IF-CL-MC 34B weight&data 82.3 75.6 72.4 61.4

Bibtex

@misc{guo2024instruction,
      title={Instruction Fusion: Advancing Prompt Evolution through Hybridization}, 
      author={Weidong Guo and Jiuding Yang and Kaitong Yang and Xiangyang Li and Zhuwei Rao and Yu Xu and Di Niu},
      year={2024},
      eprint={2312.15692},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}