README.md

metadata

license: llama2
license_name: deepseek
license_link: LICENSE
datasets:
  - wyt2000/InverseCoder-CL-7B-Evol-Instruct-90K
  - ise-uiuc/Magicoder-Evol-Instruct-110K
library_name: transformers
pipeline_tag: text-generation
tags:
  - code
model-index:
  - name: InverseCoder-CL-7B
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.762
            verified: false
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval(+)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.72
            verified: false
      - task:
          type: text-generation
        dataset:
          type: mbpp
          name: MBPP
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.706
            verified: false
      - task:
          type: text-generation
        dataset:
          type: mbpp
          name: MBPP(+)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.601
            verified: false
      - task:
          type: text-generation
        dataset:
          type: ds1000
          name: DS-1000 (Overall Completion)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.399
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (Java)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.487
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (JavaScript)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.619
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (C++)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.526
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (PHP)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.552
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (Swift)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.53
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (Rust)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.461
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (Average for non-python languages)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.529
            verified: false

InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct

InverseCoder is a series of code LLMs instruction-tuned by generating data from itself through Inverse-Instruct.

Models

	Base Model	InverseCoder	Dataset
7B	codellama/CodeLlama-7b-Python-hf	wyt2000/InverseCoder-CL-7B	wyt2000/InverseCoder-CL-7B-Evol-Instruct-90K
6.7B	deepseek-ai/deepseek-coder-6.7b-base	wyt2000/InverseCoder-DS-6.7B	wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K

Paper

Arxiv: https://arxiv.org/abs/2407.05700

Please cite the paper if you use the data or code from InverseCoder.

@misc{wu2024inversecoderunleashingpowerinstructiontuned,
      title={InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct}, 
      author={Yutong Wu and Di Huang and Wenxuan Shi and Wei Wang and Lingzhe Gao and Shihao Liu and Ziyuan Nan and Kaizhao Yuan and Rui Zhang and Xishan Zhang and Zidong Du and Qi Guo and Yewen Pu and Dawei Yin and Xing Hu and Yunji Chen},
      year={2024},
      eprint={2407.05700},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.05700}, 
}