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InverseCoder-CL-13B

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metadata
license: llama2
datasets:
  - wyt2000/InverseCoder-CL-13B-Evol-Instruct-90K
  - ise-uiuc/Magicoder-Evol-Instruct-110K
library_name: transformers
pipeline_tag: text-generation
tags:
  - code
model-index:
  - name: InverseCoder-CL-13B
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.799
            verified: false
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval(+)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.744
            verified: false
      - task:
          type: text-generation
        dataset:
          type: mbpp
          name: MBPP
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.746
            verified: false
      - task:
          type: text-generation
        dataset:
          type: mbpp
          name: MBPP(+)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.63
            verified: false
      - task:
          type: text-generation
        dataset:
          type: ds1000
          name: DS-1000 (Overall Completion)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.431
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (Java)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.545
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (JavaScript)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.654
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (C++)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.581
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (PHP)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.553
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (Swift)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.525
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (Rust)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.556
            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: 0.569
            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 and Datasets

Base Model InverseCoder Dataset
6.7B deepseek-ai/deepseek-coder-6.7b-base wyt2000/InverseCoder-DS-6.7B wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K
7B codellama/CodeLlama-7b-Python-hf wyt2000/InverseCoder-CL-7B wyt2000/InverseCoder-CL-7B-Evol-Instruct-90K
13B codellama/CodeLlama-13b-Python-hf wyt2000/InverseCoder-CL-13B <= You are here wyt2000/InverseCoder-CL-13B-Evol-Instruct-90K

Usage

Similar to Magicoder-S-DS-6.7B, use the code below to get started with the model. Make sure you installed the transformers library.

from transformers import pipeline
import torch
INVERSECODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
@@ Instruction
{instruction}
@@ Response
"""
instruction = <Your code instruction here>
prompt = INVERSECODER_PROMPT.format(instruction=instruction)
generator = pipeline(
    model="wyt2000/InverseCoder-CL-13B",
    task="text-generation",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0)
print(result[0]["generated_text"])

Paper

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

Please cite the paper if you use the models or datasets 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}, 
}

Code

Official code repo for Inverse-Instruct (under development).

Acknowledgements